CN116796248A - Forest health environment assessment system and method thereof - Google Patents
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Abstract
A forest health environment assessment system and method are disclosed. Firstly, carrying out image preprocessing on a panoramic image of a forest health environment to be evaluated, then obtaining a panoramic feature matrix through a convolutional neural network model, then carrying out feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix, then carrying out feature matrix segmentation on the optimized panoramic feature matrix to obtain a plurality of local feature matrices, then expanding the plurality of local feature matrices into a plurality of local feature vectors, then obtaining classification feature vectors through a context encoder, and finally, passing the classification feature vectors through a classifier to obtain a classification result for representing whether the plant diversity of the forest health environment to be evaluated meets a preset standard. Thus, the detection of the plant diversity of the forest health environment can be realized.
Description
Technical Field
The application relates to the field of intelligent evaluation, and more particularly relates to a forest health environment evaluation system and a forest health environment evaluation method.
Background
Forest health refers to the utilization of natural resources and ecological services of forests to provide physical and mental health protection and promotion for people. The forest health and maintenance environment is a natural environment with certain elements such as forest coverage, biodiversity, air quality, water quality, noise level, beautiful landscape and the like, and can meet health and maintenance requirements and expectations of people.
Forest health is a popular life style under the conditions of fast pace and high pressure of modern urban life. In order to effectively develop and utilize forest health resources, a scientific assessment of the forest health environment is required to determine its health value and potential, and to formulate rational management and protection measures.
Accordingly, an optimized forest health environment assessment system is desired.
Disclosure of Invention
The present application has been made to solve the above-mentioned technical problems. The embodiment of the application provides a forest health environment assessment system and a forest health environment assessment method. Firstly, carrying out image preprocessing on a panoramic image of a forest health environment to be evaluated, then obtaining a panoramic feature matrix through a convolutional neural network model, then carrying out feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix, then carrying out feature matrix segmentation on the optimized panoramic feature matrix to obtain a plurality of local feature matrices, then expanding the plurality of local feature matrices into a plurality of local feature vectors, then obtaining classification feature vectors through a context encoder, and finally, passing the classification feature vectors through a classifier to obtain a classification result for representing whether the plant diversity of the forest health environment to be evaluated meets a preset standard. Thus, the detection of the plant diversity of the forest health environment can be realized.
According to one aspect of the present application, there is provided a forest health environment assessment system comprising:
the panorama acquisition module is used for acquiring a panorama of the forest health environment to be evaluated acquired by the unmanned aerial vehicle;
the image preprocessing module is used for preprocessing the panoramic image of the forest health environment to be evaluated to obtain a preprocessed panoramic image;
the panoramic image feature extraction module is used for enabling the preprocessed panoramic image to pass through a convolutional neural network model serving as a feature extractor to obtain a panoramic feature matrix;
the feature optimization module is used for carrying out feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix;
the matrix segmentation module is used for carrying out feature matrix segmentation on the optimized panoramic feature matrix to obtain a plurality of local feature matrices;
the global image semantic coding module is used for expanding the local feature matrixes into a plurality of local feature vectors and then obtaining classification feature vectors through a context encoder based on a converter; and
the environment evaluation module is used for enabling the classification feature vectors to pass through a classifier to obtain classification results, and the classification results are used for indicating whether plant diversity of the forest health environment to be evaluated meets a preset standard or not.
In the above forest health environment assessment system, the panoramic image feature extraction module is configured to:
and respectively carrying out convolution processing, mean pooling processing along a channel dimension and nonlinear activation processing on input data in forward transfer of layers by using each layer of the convolutional neural network model serving as the feature extractor to output the panoramic feature matrix from the last layer of the convolutional neural network model serving as the feature extractor, wherein the input of the first layer of the convolutional neural network model serving as the feature extractor is the preprocessed panoramic image.
In the above forest health environment assessment system, the feature optimization module includes:
the optimization factor calculation unit is used for calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix to obtain a plurality of position information schema scene attention unbiased estimation factors; and
and the weighted optimization unit is used for weighted optimization of each position characteristic value of the panoramic characteristic matrix by taking the unbiased estimation factors of the attention of the multiple position information schema scenes as weighting coefficients so as to obtain the optimized panoramic characteristic matrix.
In the above forest health environment assessment system, the optimization factor calculation unit is configured to:
calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix by using the following optimization formula to obtain the plurality of position information schema scene attention unbiased estimation factors;
wherein, the optimization formula is:
wherein f i Is the feature value of each position in the panoramic feature matrix, (x) i ,y i ) Position coordinates for each position feature value of the panoramic feature matrix, andis the global mean value of all eigenvalues of the panoramic eigenvalue matrix,/for all eigenvalues of the panoramic eigenvalue matrix>And->Representing different functions of mapping two-dimensional real numbers into one-dimensional real numbers, W and H are the width and the height of the panoramic feature matrix, log represents a logarithmic function based on 2, and W i Each of the plurality of location information schema scene attention unbiased estimation factors is represented.
In the above forest health environment assessment system, the global image semantic coding module is configured to:
passing the plurality of local feature vectors through the converter-based context encoder to obtain a plurality of local semantic feature vectors; and
And cascading the plurality of local semantic feature vectors to obtain the classification feature vector.
In the above forest health environment assessment system, the environment assessment module includes:
the full-connection coding unit is used for carrying out full-connection coding on the classification characteristic vectors by using a plurality of full-connection layers of the classifier so as to obtain coded classification characteristic vectors; and
and the classification unit is used for passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
According to another aspect of the present application, there is provided a forest health environment assessment method, comprising:
acquiring a panoramic view of a forest health and maintenance environment to be evaluated, which is acquired by an unmanned aerial vehicle;
performing image preprocessing on the panoramic image of the forest health environment to be evaluated to obtain a preprocessed panoramic image;
the preprocessed panoramic image is passed through a convolutional neural network model serving as a feature extractor to obtain a panoramic feature matrix;
performing feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix;
performing feature matrix segmentation on the optimized panoramic feature matrix to obtain a plurality of local feature matrices;
The local feature matrixes are unfolded to be a plurality of local feature vectors, and then the local feature vectors are processed by a context encoder based on a converter to obtain classified feature vectors; and
and the classification feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the plant diversity of the forest health and maintenance environment to be evaluated meets a preset standard.
In the above forest health environment assessment method, the step of obtaining a panoramic feature matrix from the preprocessed panoramic image by using a convolutional neural network model as a feature extractor includes:
and respectively carrying out convolution processing, mean pooling processing along a channel dimension and nonlinear activation processing on input data in forward transfer of layers by using each layer of the convolutional neural network model serving as the feature extractor to output the panoramic feature matrix from the last layer of the convolutional neural network model serving as the feature extractor, wherein the input of the first layer of the convolutional neural network model serving as the feature extractor is the preprocessed panoramic image.
In the above forest health environment assessment method, performing feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix, including:
Calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix to obtain a plurality of position information schema scene attention unbiased estimation factors; and
and carrying out weighted optimization on each position characteristic value of the panoramic characteristic matrix by taking the unbiased estimation factors of the attention of the multiple position information schema scenes as weighting coefficients to obtain the optimized panoramic characteristic matrix.
In the above forest health environment assessment method, calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix to obtain a plurality of position information schema scene attention unbiased estimation factors includes:
calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix by using the following optimization formula to obtain the plurality of position information schema scene attention unbiased estimation factors;
wherein, the optimization formula is:
wherein f i Is the feature value of each position in the panoramic feature matrix, (x) i ,y i ) Position coordinates for each position feature value of the panoramic feature matrix, andis the global mean value of all eigenvalues of the panoramic eigenvalue matrix,/for all eigenvalues of the panoramic eigenvalue matrix >And->Representing different functions of mapping two-dimensional real numbers into one-dimensional real numbers, W and H are the width and the height of the panoramic feature matrix, log represents a logarithmic function based on 2, and W i Each of the plurality of location information schema scene attention unbiased estimation factors is represented.
Compared with the prior art, the forest health environment assessment system and the method thereof provided by the application have the advantages that firstly, the panoramic image of the forest health environment to be assessed is subjected to image preprocessing, then the panoramic image is subjected to a convolutional neural network model to obtain a panoramic feature matrix, then, the panoramic feature matrix is subjected to feature distribution optimization to obtain an optimized panoramic feature matrix, then, the optimized panoramic feature matrix is subjected to feature matrix segmentation to obtain a plurality of local feature matrices, then, the local feature matrices are unfolded into a plurality of local feature vectors, and then, the local feature vectors are subjected to a context encoder to obtain classification feature vectors, and finally, the classification feature vectors are subjected to a classifier to obtain classification results for indicating whether the plant diversity of the forest health environment to be assessed meets the preset standard. Thus, the detection of the plant diversity of the forest health environment can be realized.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art. The following drawings are not intended to be drawn to scale, emphasis instead being placed upon illustrating the principles of the application.
Fig. 1 is an application scenario diagram of a forest health environment assessment system according to an embodiment of the present application.
FIG. 2 is a block diagram schematic of a forest health environment assessment system in accordance with an embodiment of the present application.
FIG. 3 is a block diagram schematic of the feature optimization module in the forest health environment assessment system according to an embodiment of the present application.
FIG. 4 is a block diagram schematic of the environmental assessment module in a forest health environmental assessment system according to an embodiment of the present application.
FIG. 5 is a flow chart of a forest health environment assessment method according to an embodiment of the present application.
Fig. 6 is a schematic diagram of a system architecture of a forest health environment assessment method according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are also within the scope of the application.
As used in the specification and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Although the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server. The modules are merely illustrative, and different aspects of the systems and methods may use different modules.
A flowchart is used in the present application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
As described above, forest health is a popular lifestyle in the case of fast pace and high pressure of modern urban life. In order to effectively develop and utilize forest health resources, a scientific assessment of the forest health environment is required to determine its health value and potential, and to formulate rational management and protection measures. Accordingly, an optimized forest health environment assessment system is desired.
Accordingly, in consideration of the fact that in the evaluation process of the forest health environment, the key is to analyze and detect the plant diversity of the forest health environment so as to ensure that the plant diversity meets the preset requirement. Based on this, in the technical scheme of the application, it is desirable to acquire a panoramic image of a forest health environment to be evaluated through an unmanned aerial vehicle, and evaluate whether the plant diversity of the forest health environment to be evaluated meets a predetermined standard by performing machine vision-based image analysis and processing on the panoramic image. However, since a large amount of useless interference information exists in the panorama of the forest health environment to be evaluated, which is collected by the unmanned aerial vehicle, and plant category characteristic information about the forest health environment is hidden characteristic information with a small scale in an image, the effective capture is difficult to be performed through a traditional characteristic extraction mode. Therefore, in the process, the difficulty is how to fully express the plant category implicit characteristic information about the forest health and maintenance environment in the panoramic view of the forest health and maintenance environment to be evaluated, so as to detect the plant diversity of the forest health and maintenance environment, thereby determining the health and maintenance value and potential thereof and formulating reasonable management and protection measures.
In recent years, deep learning and neural networks have been widely used in the fields of computer vision, natural language processing, text signal processing, and the like. The development of deep learning and neural networks provides new solutions and schemes for mining plant category implicit characteristic information about the forest health environment in the panoramic view of the forest health environment to be evaluated.
Specifically, in the technical scheme of the application, firstly, a panorama of a forest health environment to be evaluated, which is acquired by an unmanned aerial vehicle, is acquired. It should be understood that, in the process of actually performing the acquisition of the panorama of the forest health environment to be evaluated, the acquired original image may be affected by factors such as noise, deformation or edge distortion, which may affect the accuracy and stability of the subsequent processing algorithm. Therefore, before the feature extraction of the panorama of the forest health environment to be evaluated is performed to perform the plant diversity detection of the forest health environment to be evaluated, the panorama of the forest health environment to be evaluated needs to be subjected to image preprocessing to obtain a preprocessed panorama, so as to remove noise, smooth edges, correct nonlinear effects and the like in the original image. That is, through such a preprocessing process, a clearer, accurate and stable panorama can be obtained, which contributes to improving the accuracy of the subsequent classification.
Then, feature mining of the pre-processed panorama is performed using a convolutional neural network model as a feature extractor having excellent performance in terms of implicit feature extraction of images, thereby extracting implicit feature distribution information about plant categories of forest health environments in the pre-processed panorama, and thus obtaining a panorama feature matrix.
Further, it is considered that the pure CNN method is difficult to learn explicit global and remote semantic information interactions due to inherent limitations of convolution operations, although the local implicit feature distribution information about the plant class of the forest health environment in the pre-processed panorama can be extracted due to the convolutional neural network model as the feature extractor. Therefore, in the technical scheme of the application, after the feature matrix segmentation is further carried out on the panoramic feature matrix to obtain a plurality of local feature matrices, the local feature matrices are unfolded to be a plurality of local feature vectors, and then the local feature vectors are encoded in a context encoder based on a converter, so that all local implicit features of plant categories related to a forest health environment in the preprocessed panoramic image are extracted based on global context image semantic association feature information, and thus classification feature vectors are obtained.
And then, further classifying the classification characteristic vector by a classifier to obtain a classification result used for indicating whether the plant diversity of the forest health and maintenance environment to be evaluated meets a preset standard. That is, in the technical solution of the present application, the labels of the classifier include that the plant diversity of the forest health environment to be evaluated meets a predetermined criterion (first label), and that the plant diversity of the forest health environment to be evaluated does not meet a predetermined criterion (second label), wherein the classifier determines to which classification label the classification feature vector belongs through a soft maximum function. It should be noted that the first tag p1 and the second tag p2 do not include a manually set concept, and in fact, during the training process, the computer model does not have a concept of "whether the plant diversity of the forest health environment to be evaluated meets the predetermined standard" which is simply that there are two kinds of classification tags and the probability that the output feature is under the two kinds of classification tags, that is, the sum of p1 and p2 is one. Therefore, the classification result of whether the plant diversity of the forest health and maintenance environment to be evaluated meets the preset standard is actually converted into the classified probability distribution conforming to the natural rule through the classification label, and the physical meaning of the natural probability distribution of the label is essentially used instead of the language text meaning of whether the plant diversity of the forest health and maintenance environment to be evaluated meets the preset standard. It should be understood that, in the technical solution of the present application, the classification label of the classifier is a detection and evaluation label for determining whether the plant diversity of the forest health and maintenance environment to be evaluated meets a predetermined standard, so after the classification result is obtained, the plant diversity of the forest health and maintenance environment can be detected based on the classification result, thereby determining the health and maintenance value and potential thereof.
In particular, in the technical scheme of the application, the feature value of each position of the panoramic feature matrix obtained by taking into consideration that the source image semantics of each pixel of the panoramic image is extracted through the image semantic association features of the convolutional neural network model serving as the feature extractor also has a corresponding position attribute, so that the plurality of local feature matrices obtained by carrying out feature matrix segmentation on the panoramic feature matrix have position association expression attributes in the matrix and among the matrices.
However, when the local feature matrices are expanded into the local feature vectors, the local feature matrix is aggregated by location, and the context encoder based on the converter basically ignores the location attribute when performing context semantic coding, so that the expression effect of each feature value of the panoramic feature matrix on the original feature manifold of the panoramic feature matrix when aggregating by location needs to be improved.
Based on this, the applicant of the present application calculates a location information schema scene attention unbiased estimation factor of the eigenvalues of each location of the panoramic eigenvalue matrix, expressed as:
wherein the method comprises the steps ofAnd- >Respectively representDifferent functions mapping two-dimensional real numbers to one-dimensional real numbers, e.g. implementing a representation of activation weights and biasing for non-linear activation functions, W and H being the width and height, respectively, of the panoramic feature matrix, (x) i ,y i ) For each eigenvalue f of the panoramic eigenvalue matrix i For example, it may be any vertex or center of the feature matrix as origin of coordinates, and +.>Is the global average of all eigenvalues of the panoramic eigenvalue matrix.
Here, the position information schema type scene attention unbiased estimation factor further performs shape information aggregation of feature manifolds when the feature values are aggregated by positions of the whole feature distribution by using a schema information representation of relative geometric directions and relative geometric distances of fusion feature values relative to high-dimensional space positions of the whole feature distribution and a higher-order feature representation of information representation of the high-dimensional feature itself, so as to realize unbiased estimation of scene geometry of the distribution based on various sub-manifold aggregate shapes of the feature manifolds in the high-dimensional space, so as to accurately express geometric properties of manifold shapes of the feature matrix. In this way, the feature values of all the positions of the panoramic feature matrix are weighted by the position information schema scene attention unbiased estimation factors, so that the expression effect of all the feature values of the panoramic feature matrix on the original feature manifold of the panoramic feature matrix in position aggregation can be improved, and the feature expression effect of the classification feature vector obtained from the global feature matrix is improved, so that the accuracy of the classification result obtained by the classifier is improved. Therefore, the plant diversity of the forest health and maintenance environment can be more accurately detected and evaluated, so that the health and maintenance value and potential of the forest health and maintenance environment are determined to formulate reasonable management and protection measures.
Fig. 1 is an application scenario diagram of a forest health environment assessment system according to an embodiment of the present application. As shown in fig. 1, in this application scenario, first, a panorama of a forest health environment to be evaluated (e.g., D illustrated in fig. 1) acquired by an unmanned aerial vehicle (e.g., N illustrated in fig. 1) is acquired, and then, the panorama of the forest health environment to be evaluated is input into a server (e.g., S illustrated in fig. 1) where a forest health environment evaluation algorithm is deployed, wherein the server is capable of processing the panorama of the forest health environment to be evaluated using the forest health environment evaluation algorithm to obtain a classification result for indicating whether plant diversity of the forest health environment to be evaluated meets a predetermined standard.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
FIG. 2 is a block diagram schematic of a forest health environment assessment system in accordance with an embodiment of the present application. As shown in fig. 2, the forest health environment assessment system 100 according to the embodiment of the present application includes: a panorama acquisition module 110, configured to acquire a panorama of a forest health environment to be evaluated acquired by the unmanned aerial vehicle; the image preprocessing module 120 is configured to perform image preprocessing on the panorama of the forest health environment to be evaluated to obtain a preprocessed panorama; the panoramic image feature extraction module 130 is configured to pass the preprocessed panoramic image through a convolutional neural network model serving as a feature extractor to obtain a panoramic feature matrix; the feature optimization module 140 is configured to perform feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix; the matrix segmentation module 150 is configured to perform feature matrix segmentation on the optimized panoramic feature matrix to obtain a plurality of local feature matrices; the global image semantic coding module 160 is configured to obtain a classification feature vector by using a context encoder based on a converter after expanding the plurality of local feature matrices into a plurality of local feature vectors; and an environment evaluation module 170, configured to pass the classification feature vector through a classifier to obtain a classification result, where the classification result is used to indicate whether the plant diversity of the forest health environment to be evaluated meets a predetermined criterion.
More specifically, in the embodiment of the present application, the panorama acquisition module 110 is configured to acquire a panorama of the forest health environment to be evaluated acquired by the unmanned aerial vehicle. In the actual evaluation process of the forest health and maintenance environment, the key point is to analyze and detect the plant diversity of the forest health and maintenance environment so as to ensure that the plant diversity meets the preset requirement. Based on the above, in the technical scheme of the application, the panorama of the forest health environment to be evaluated can be acquired through the unmanned aerial vehicle, and whether the plant diversity of the forest health environment to be evaluated meets the preset standard is evaluated by performing image analysis and processing on the panorama based on machine vision.
More specifically, in the embodiment of the present application, the image preprocessing module 120 is configured to perform image preprocessing on the panorama of the forest health environment to be evaluated to obtain a preprocessed panorama. In the actual process of collecting the panoramic image of the forest health and maintenance environment to be evaluated, the collected original image may be affected by factors such as noise, deformation or edge distortion, which may affect the accuracy and stability of the subsequent processing algorithm. Therefore, before the feature extraction of the panorama of the forest health environment to be evaluated is performed to perform the plant diversity detection of the forest health environment to be evaluated, the panorama of the forest health environment to be evaluated needs to be subjected to image preprocessing to obtain a preprocessed panorama, so as to remove noise, smooth edges, correct nonlinear effects and the like in the original image. That is, through such a preprocessing process, a clearer, accurate and stable panorama can be obtained, which contributes to improving the accuracy of the subsequent classification.
More specifically, in the embodiment of the present application, the panorama image feature extraction module 130 is configured to pass the preprocessed panorama image through a convolutional neural network model as a feature extractor to obtain a panorama feature matrix. And performing feature mining of the preprocessed panoramic image by using a convolutional neural network model which is taken as a feature extractor and has excellent performance in the aspect of implicit feature extraction of the image, so as to extract implicit feature distribution information about plant categories of a forest health environment in the preprocessed panoramic image, thereby obtaining a panoramic feature matrix.
It should be appreciated that convolutional neural network (Convolutional Neural Network, CNN) is an artificial neural network and has wide application in the fields of image recognition and the like. The convolutional neural network may include an input layer, a hidden layer, and an output layer, where the hidden layer may include a convolutional layer, a pooling layer, an activation layer, a full connection layer, etc., where the previous layer performs a corresponding operation according to input data, outputs an operation result to the next layer, and obtains a final result after the input initial data is subjected to a multi-layer operation.
Accordingly, in one specific example, the panoramic image feature extraction module 130 is configured to: and respectively carrying out convolution processing, mean pooling processing along a channel dimension and nonlinear activation processing on input data in forward transfer of layers by using each layer of the convolutional neural network model serving as the feature extractor to output the panoramic feature matrix from the last layer of the convolutional neural network model serving as the feature extractor, wherein the input of the first layer of the convolutional neural network model serving as the feature extractor is the preprocessed panoramic image.
More specifically, in the embodiment of the present application, the feature optimization module 140 is configured to perform feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix. In particular, in the technical scheme of the application, the feature value of each position of the panoramic feature matrix obtained by taking into consideration that the source image semantics of each pixel of the panoramic image is extracted through the image semantic association features of the convolutional neural network model serving as the feature extractor also has a corresponding position attribute, so that the plurality of local feature matrices obtained by carrying out feature matrix segmentation on the panoramic feature matrix have position association expression attributes in the matrix and among the matrices. However, when the local feature matrices are expanded into the local feature vectors, the local feature matrix is aggregated by location, and the context encoder based on the converter basically ignores the location attribute when performing context semantic coding, so that the expression effect of each feature value of the panoramic feature matrix on the original feature manifold of the panoramic feature matrix when aggregating by location needs to be improved. Based on this, the applicant of the present application calculated a location information schema scene attention unbiased estimation factor of the eigenvalues of each location of the panoramic eigenvector.
Accordingly, in one specific example, as shown in fig. 3, the feature optimization module 140 includes: an optimization factor calculating unit 141, configured to calculate a position information schema scene attention unbiased estimation factor of each position feature value in the panoramic feature matrix to obtain a plurality of position information schema scene attention unbiased estimation factors; and a weighted optimization unit 142, configured to perform weighted optimization on each position feature value of the panoramic feature matrix with the unbiased estimation factors of attention of the scene in the plurality of position information drawings as weighting coefficients to obtain the optimized panoramic feature matrix.
Accordingly, in a specific example, the optimization factor calculating unit 141 is configured to: calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix by using the following optimization formula to obtain the plurality of position information schema scene attention unbiased estimation factors; wherein, the optimization formula is:
wherein f i Is the feature value of each position in the panoramic feature matrix, (x) i ,y i ) Position coordinates for each position feature value of the panoramic feature matrix, andis the global mean value of all eigenvalues of the panoramic eigenvalue matrix,/for all eigenvalues of the panoramic eigenvalue matrix >And->Representing different functions of mapping two-dimensional real numbers into one-dimensional real numbers, W and H are the width and the height of the panoramic feature matrix, log represents a logarithmic function based on 2, and W i Each of the plurality of location information schema scene attention unbiased estimation factors is represented.
Here, the position information schema type scene attention unbiased estimation factor further performs shape information aggregation of feature manifolds when the feature values are aggregated by positions of the whole feature distribution by using a schema information representation of relative geometric directions and relative geometric distances of fusion feature values relative to high-dimensional space positions of the whole feature distribution and a higher-order feature representation of information representation of the high-dimensional feature itself, so as to realize unbiased estimation of scene geometry of the distribution based on various sub-manifold aggregate shapes of the feature manifolds in the high-dimensional space, so as to accurately express geometric properties of manifold shapes of the feature matrix. In this way, the feature values of all the positions of the panoramic feature matrix are weighted by the position information schema scene attention unbiased estimation factors, so that the expression effect of all the feature values of the panoramic feature matrix on the original feature manifold of the panoramic feature matrix in position aggregation can be improved, and the feature expression effect of the classification feature vector obtained from the global feature matrix is improved, so that the accuracy of the classification result obtained by the classifier is improved. Therefore, the plant diversity of the forest health and maintenance environment can be more accurately detected and evaluated, so that the health and maintenance value and potential of the forest health and maintenance environment are determined to formulate reasonable management and protection measures.
More specifically, in the embodiment of the present application, the matrix segmentation module 150 is configured to perform feature matrix segmentation on the optimized panoramic feature matrix to obtain a plurality of local feature matrices. Although the convolutional neural network model serving as the feature extractor can extract the local implicit feature distribution information about the plant category of the forest health environment in the preprocessed panoramic image, the pure CNN method is difficult to learn explicit global and remote semantic information interaction due to the inherent limitation of convolution operation. Therefore, in the technical scheme of the application, after the feature matrix segmentation is further carried out on the optimized panoramic feature matrix to obtain a plurality of local feature matrices, the local feature matrices are unfolded to be a plurality of local feature vectors, and then the local feature vectors are encoded in a context encoder based on a converter, so that all local implicit features of plant categories related to a forest health environment in the preprocessed panoramic image are extracted based on global context image semantic association feature information, and thus classification feature vectors are obtained.
More specifically, in an embodiment of the present application, the global image semantic coding module 160 is configured to obtain the classification feature vector by a context encoder based on a converter after expanding the plurality of local feature matrices into a plurality of local feature vectors.
Accordingly, in one specific example, the global image semantic encoding module 160 is configured to: passing the plurality of local feature vectors through the converter-based context encoder to obtain a plurality of local semantic feature vectors; and cascading the plurality of local semantic feature vectors to obtain the classification feature vector.
More specifically, in the embodiment of the present application, the environment evaluation module 170 is configured to pass the classification feature vector through a classifier to obtain a classification result, where the classification result is used to indicate whether the plant diversity of the forest health environment to be evaluated meets a predetermined criterion. After the classification result is obtained, the plant diversity of the forest health environment can be detected based on the classification result, so that the health value and potential of the forest health environment can be determined.
It should be appreciated that the role of the classifier is to learn the classification rules and classifier using a given class, known training data, and then classify (or predict) the unknown data. Logistic regression (logistics), SVM, etc. are commonly used to solve the classification problem, and for multi-classification problems (multi-class classification), logistic regression or SVM can be used as well, but multiple bi-classifications are required to compose multiple classifications, but this is error-prone and inefficient, and the commonly used multi-classification method is the Softmax classification function.
Accordingly, in one specific example, as shown in fig. 4, the environment assessment module 170 includes: a full-connection encoding unit 171, configured to perform full-connection encoding on the classification feature vector by using a plurality of full-connection layers of the classifier to obtain an encoded classification feature vector; and a classification unit 172, configured to pass the encoded classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
In summary, the forest health environment assessment system 100 according to the embodiment of the present application is illustrated, firstly, an image preprocessing is performed on a panorama of a forest health environment to be assessed, then the panorama passes through a convolutional neural network model to obtain a panorama feature matrix, then, feature distribution optimization is performed on the panorama feature matrix to obtain an optimized panorama feature matrix, then, feature matrix segmentation is performed on the optimized panorama feature matrix to obtain a plurality of local feature matrices, then, the plurality of local feature matrices are expanded into a plurality of local feature vectors, and then, the plurality of local feature matrices pass through a context encoder to obtain classification feature vectors, and finally, the classification feature vectors pass through a classifier to obtain classification results for indicating whether plant diversity of the forest health environment to be assessed meets a predetermined standard. Thus, the detection of the plant diversity of the forest health environment can be realized.
As described above, the forest health environment assessment system 100 according to the embodiment of the present application may be implemented in various terminal devices, for example, a server or the like having a forest health environment assessment algorithm according to the embodiment of the present application. In one example, the forest health environment assessment system 100 in accordance with an embodiment of the present application may be integrated into the terminal device as a software module and/or hardware module. For example, the forest health environment assessment system 100 according to an embodiment of the present application may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the forest health environment assessment system 100 according to an embodiment of the present application may also be one of a plurality of hardware modules of the terminal device.
Alternatively, in another example, the forest health environment assessment system 100 and the terminal device according to the embodiment of the present application may be separate devices, and the forest health environment assessment system 100 may be connected to the terminal device through a wired and/or wireless network, and transmit interactive information in a contracted data format.
FIG. 5 is a flow chart of a forest health environment assessment method according to an embodiment of the present application. As shown in fig. 5, the forest health environment assessment method according to the embodiment of the application includes: s110, acquiring a panoramic view of a forest health environment to be evaluated, which is acquired by an unmanned aerial vehicle; s120, performing image preprocessing on the panoramic image of the forest health environment to be evaluated to obtain a preprocessed panoramic image; s130, passing the preprocessed panoramic image through a convolutional neural network model serving as a feature extractor to obtain a panoramic feature matrix; s140, performing feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix; s150, feature matrix segmentation is carried out on the optimized panoramic feature matrix to obtain a plurality of local feature matrices; s160, the local feature matrixes are unfolded to be a plurality of local feature vectors, and then the local feature vectors are processed by a context encoder based on a converter to obtain classified feature vectors; and S170, passing the classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the plant diversity of the forest health environment to be evaluated meets a preset standard.
Fig. 6 is a schematic diagram of a system architecture of a forest health environment assessment method according to an embodiment of the present application. As shown in fig. 6, in the system architecture of the forest health environment assessment method, firstly, a panorama of a forest health environment to be assessed, which is collected by an unmanned aerial vehicle, is acquired; then, carrying out image preprocessing on the panoramic image of the forest health environment to be evaluated to obtain a preprocessed panoramic image; then, the preprocessed panoramic image passes through a convolutional neural network model serving as a feature extractor to obtain a panoramic feature matrix; then, carrying out feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix; then, feature matrix segmentation is carried out on the optimized panoramic feature matrix to obtain a plurality of local feature matrices; then, the local feature matrixes are unfolded into local feature vectors, and the local feature vectors are processed by a context encoder based on a converter to obtain classified feature vectors; and finally, the classification feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the plant diversity of the forest health and maintenance environment to be evaluated meets a preset standard.
In a specific example, in the above forest health environment assessment method, passing the preprocessed panorama through a convolutional neural network model as a feature extractor to obtain a panorama feature matrix, including: and respectively carrying out convolution processing, mean pooling processing along a channel dimension and nonlinear activation processing on input data in forward transfer of layers by using each layer of the convolutional neural network model serving as the feature extractor to output the panoramic feature matrix from the last layer of the convolutional neural network model serving as the feature extractor, wherein the input of the first layer of the convolutional neural network model serving as the feature extractor is the preprocessed panoramic image.
In a specific example, in the forest health environment assessment method, performing feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix includes: calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix to obtain a plurality of position information schema scene attention unbiased estimation factors; and weighting and optimizing each position characteristic value of the panoramic characteristic matrix by taking the unbiased estimation factors of the attention of the multiple position information schema scenes as weighting coefficients to obtain the optimized panoramic characteristic matrix.
In a specific example, in the forest health environment assessment method, calculating the location information schema type attention unbiased estimation factors of each location feature value in the panoramic feature matrix to obtain a plurality of location information schema type attention unbiased estimation factors includes: calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix by using the following optimization formula to obtain the plurality of position information schema scene attention unbiased estimation factors; wherein, the optimization formula is:
wherein f i Is the feature value of each position in the panoramic feature matrix, (x) i ,y i ) Position coordinates for each position feature value of the panoramic feature matrix, andis the global mean value of all eigenvalues of the panoramic eigenvalue matrix,/for all eigenvalues of the panoramic eigenvalue matrix>And->Representing different functions of mapping two-dimensional real numbers into one-dimensional real numbers, W and H are the width and the height of the panoramic feature matrix, log represents a logarithmic function based on 2, and W i Each of the plurality of location information schema scene attention unbiased estimation factors is represented.
In a specific example, in the above forest health environment assessment method, the step of obtaining the classification feature vector by a context encoder based on a converter after expanding the plurality of local feature matrices into a plurality of local feature vectors includes: passing the plurality of local feature vectors through the converter-based context encoder to obtain a plurality of local semantic feature vectors; and cascading the plurality of local semantic feature vectors to obtain the classification feature vector.
In a specific example, in the above forest health environment assessment method, the classifying feature vector is passed through a classifier to obtain a classification result, where the classification result is used to indicate whether the plant diversity of the forest health environment to be assessed meets a predetermined criterion, and the method includes: performing full-connection coding on the classification feature vectors by using a plurality of full-connection layers of the classifier to obtain coded classification feature vectors; and passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
Here, it will be understood by those skilled in the art that the specific operations of the respective steps in the above-described forest health environment assessment method have been described in detail in the above description of the forest health environment assessment system 100 with reference to fig. 1 to 4, and thus, repetitive descriptions thereof will be omitted.
According to another aspect of the present application there is also provided a non-volatile computer readable storage medium having stored thereon computer readable instructions which when executed by a computer can perform a method as described above.
Program portions of the technology may be considered to be "products" or "articles of manufacture" in the form of executable code and/or associated data, embodied or carried out by a computer readable medium. A tangible, persistent storage medium may include any memory or storage used by a computer, processor, or similar device or related module. Such as various semiconductor memories, tape drives, disk drives, or the like, capable of providing storage functionality for software.
The application uses specific words to describe embodiments of the application. Reference to "a first/second embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof. Although a few exemplary embodiments of this invention have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this invention. Accordingly, all such modifications are intended to be included within the scope of this invention as defined in the following claims. It is to be understood that the foregoing is illustrative of the present invention and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The invention is defined by the claims and their equivalents.
Claims (10)
1. A forest health environment assessment system, comprising:
the panorama acquisition module is used for acquiring a panorama of the forest health environment to be evaluated acquired by the unmanned aerial vehicle;
the image preprocessing module is used for preprocessing the panoramic image of the forest health environment to be evaluated to obtain a preprocessed panoramic image;
the panoramic image feature extraction module is used for enabling the preprocessed panoramic image to pass through a convolutional neural network model serving as a feature extractor to obtain a panoramic feature matrix;
the feature optimization module is used for carrying out feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix;
the matrix segmentation module is used for carrying out feature matrix segmentation on the optimized panoramic feature matrix to obtain a plurality of local feature matrices;
the global image semantic coding module is used for expanding the local feature matrixes into a plurality of local feature vectors and then obtaining classification feature vectors through a context encoder based on a converter; and
the environment evaluation module is used for enabling the classification feature vectors to pass through a classifier to obtain classification results, and the classification results are used for indicating whether plant diversity of the forest health environment to be evaluated meets a preset standard or not.
2. The forest health environment assessment system of claim 1, wherein the panoramic image feature extraction module is configured to:
and respectively carrying out convolution processing, mean pooling processing along a channel dimension and nonlinear activation processing on input data in forward transfer of layers by using each layer of the convolutional neural network model serving as the feature extractor to output the panoramic feature matrix from the last layer of the convolutional neural network model serving as the feature extractor, wherein the input of the first layer of the convolutional neural network model serving as the feature extractor is the preprocessed panoramic image.
3. The forest health environment assessment system of claim 2, wherein the feature optimization module comprises:
the optimization factor calculation unit is used for calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix to obtain a plurality of position information schema scene attention unbiased estimation factors; and
and the weighted optimization unit is used for weighted optimization of each position characteristic value of the panoramic characteristic matrix by taking the unbiased estimation factors of the attention of the multiple position information schema scenes as weighting coefficients so as to obtain the optimized panoramic characteristic matrix.
4. A forest health environment assessment system according to claim 3, wherein said optimization factor calculation unit is configured to:
calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix by using the following optimization formula to obtain the plurality of position information schema scene attention unbiased estimation factors;
wherein, the optimization formula is:
wherein f i Is the feature value of each position in the panoramic feature matrix, (x) i ,y i ) Position coordinates for each position feature value of the panoramic feature matrix, andis the global mean value of all eigenvalues of the panoramic eigenvalue matrix,/for all eigenvalues of the panoramic eigenvalue matrix>Andrepresenting different functions of mapping two-dimensional real numbers into one-dimensional real numbers, W and H are the width and the height of the panoramic feature matrix, log represents a logarithmic function based on 2, and W i Each of the plurality of location information schema scene attention unbiased estimation factors is represented.
5. The forest health environment assessment system according to claim 4, wherein the global image semantic coding module is configured to:
passing the plurality of local feature vectors through the converter-based context encoder to obtain a plurality of local semantic feature vectors; and
And cascading the plurality of local semantic feature vectors to obtain the classification feature vector.
6. The forest health environment assessment system of claim 5, wherein the environment assessment module comprises:
the full-connection coding unit is used for carrying out full-connection coding on the classification characteristic vectors by using a plurality of full-connection layers of the classifier so as to obtain coded classification characteristic vectors; and
and the classification unit is used for passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
7. A forest health environment assessment method, comprising:
acquiring a panoramic view of a forest health and maintenance environment to be evaluated, which is acquired by an unmanned aerial vehicle;
performing image preprocessing on the panoramic image of the forest health environment to be evaluated to obtain a preprocessed panoramic image;
the preprocessed panoramic image is passed through a convolutional neural network model serving as a feature extractor to obtain a panoramic feature matrix;
performing feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix;
performing feature matrix segmentation on the optimized panoramic feature matrix to obtain a plurality of local feature matrices;
The local feature matrixes are unfolded to be a plurality of local feature vectors, and then the local feature vectors are processed by a context encoder based on a converter to obtain classified feature vectors; and
and the classification feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the plant diversity of the forest health and maintenance environment to be evaluated meets a preset standard.
8. The forest health environment assessment method according to claim 7, wherein passing the preprocessed panorama through a convolutional neural network model as a feature extractor to obtain a panorama feature matrix, comprising:
and respectively carrying out convolution processing, mean pooling processing along a channel dimension and nonlinear activation processing on input data in forward transfer of layers by using each layer of the convolutional neural network model serving as the feature extractor to output the panoramic feature matrix from the last layer of the convolutional neural network model serving as the feature extractor, wherein the input of the first layer of the convolutional neural network model serving as the feature extractor is the preprocessed panoramic image.
9. The forest health environment assessment method according to claim 8, wherein performing feature distribution optimization on the panoramic feature matrix to obtain an optimized panoramic feature matrix comprises:
Calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix to obtain a plurality of position information schema scene attention unbiased estimation factors; and
and carrying out weighted optimization on each position characteristic value of the panoramic characteristic matrix by taking the unbiased estimation factors of the attention of the multiple position information schema scenes as weighting coefficients to obtain the optimized panoramic characteristic matrix.
10. The method of forest health environment assessment according to claim 9, wherein calculating the location information schema scene attention unbiased estimation factors for each location feature value in the panoramic feature matrix to obtain a plurality of location information schema scene attention unbiased estimation factors, comprises:
calculating the position information schema scene attention unbiased estimation factors of each position feature value in the panoramic feature matrix by using the following optimization formula to obtain the plurality of position information schema scene attention unbiased estimation factors;
wherein, the optimization formula is:
wherein f i Is the feature value of each position in the panoramic feature matrix, (x) i ,y i ) Position coordinates for each position feature value of the panoramic feature matrix, and Is the global mean value of all eigenvalues of the panoramic eigenvalue matrix,/for all eigenvalues of the panoramic eigenvalue matrix>Andrepresenting different functions of mapping two-dimensional real numbers into one-dimensional real numbers, W and H are the width and the height of the panoramic feature matrix, log represents a logarithmic function based on 2, and W i Each of the plurality of location information schema scene attention unbiased estimation factors is represented.
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| CN118470881A (en) * | 2024-05-16 | 2024-08-09 | 江苏联通智能控制技术股份有限公司 | Fire monitoring alarm system and method based on fire risk grade intelligent evaluation |
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| CN117540908A (en) * | 2023-11-07 | 2024-02-09 | 北京佳格天地科技有限公司 | Agricultural resource integration method and system based on big data |
| CN117540908B (en) * | 2023-11-07 | 2024-06-11 | 北京佳格天地科技有限公司 | Agricultural resource integration method and system based on big data |
| CN118470881A (en) * | 2024-05-16 | 2024-08-09 | 江苏联通智能控制技术股份有限公司 | Fire monitoring alarm system and method based on fire risk grade intelligent evaluation |
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