CN110569871B - Saddle point identification method based on deep convolutional neural network - Google Patents
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Abstract
The invention discloses a saddle point identification method based on a deep convolutional neural network, which comprises the following steps: 1) constructing a training sample set; 2) establishing an improved saddle feature deep learning convolutional neural network model; 3) setting various parameters required for training the improved saddle feature deep learning convolutional neural network model, training the improved saddle feature deep learning convolutional neural network model to enable the improved saddle feature deep learning convolutional neural network model to automatically identify potential saddle points in DEM data, correcting coordinates of the saddle points through a position regression MLP network, and determining initially selected saddle points; 4) and selecting a threshold with the recognition probability larger than a set threshold from the initially selected saddle points as a final saddle point, extracting the corrected coordinates of the final saddle point, and mapping the corrected coordinates of the final saddle point back to the original DEM to obtain a final saddle point identification map.
Description
Technical Field
The invention belongs to the technical field of element recognition in the field of digital terrain analysis, and relates to a saddle point recognition method based on a deep convolutional neural network.
Background
The digital terrain analysis takes a digital elevation model DEM as a basic data source, calculates terrain attributes, extracts terrain elements, and quantitatively analyzes the change of the earth surface form at multiple angles, thereby providing method support for scientific research and application related to geoscience. Among various types of topographic elements, regional topographic attributes, topographic structure lines, and the like have been analyzed and studied in detail for a long time. However, elements such as a mountain top, a saddle, a back slope, a basin and the like are limited by difficulties such as semantic ambiguity, multi-scale features, uncertain space support domain and the like, and the automatic extraction method becomes an attention point for digital terrain analysis research.
In the existing saddle extraction method, a local analysis window is mostly adopted to analyze the geometrical morphological characteristics of the saddle, and saddle points are extracted by calculating various surface factors and the interrelation among the factors in the analysis window. For example, a DEM-based mountain saddle point hierarchical extraction method [ J ] mapping science, 2013,38(2): 181-; the document 'Kongyuanping, Yi Wei, Zhaopng' uses topological relation to rapidly extract saddle point [ J ]. computer engineering and application, 2013,49(16): 165-. The key of the methods lies in establishing a proper terrain element analysis neighborhood or topological relation description model, but the method can not pay attention to macroscopic and microscopic features such as saddle point space extension structures and element semantics, and realizes automatic element identification by establishing a deep learning neural network of saddle features.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a saddle point identification method based on a deep convolutional neural network, which can automatically identify saddle points in DEM data through a deep learning neural network of saddle features.
In order to achieve the purpose, the saddle point identification method based on the deep convolutional neural network comprises the following steps:
1) constructing a training sample set, said trainingThe training sample set is formed by m + n DEM mountain data, wherein m is n, and n is a positive sample data set A which comprises a saddle area 1 ，A 2 ，A 3 ，…，A n M is a negative sample data set B not including the saddle region { B ═ B 1 ，B 2 ，B 3 ，…，B m Rectangular cutting is carried out on DEM sample data, coordinates of saddle points in the positive sample are marked, and positive and negative sample data are divided into a training set and a verification set according to a set proportion;
2) analyzing the spatial morphological structure of the saddle, and establishing an improved saddle feature deep learning convolutional neural network model;
3) setting various parameters required by training an improved saddle feature deep learning convolutional neural network model, wherein the various parameters comprise a target function, an optimization algorithm and a loss function, and pre-training the Lenet-5 network model by means of an ImageNet data set until the output of the Lenet-5 network model is optimal and reaches a stable state; initializing an improved saddle feature deep learning convolutional neural network model by using a pre-trained Lenet-5 network model, and then training the improved saddle feature deep learning convolutional neural network model by using the training set of positive sample data and the training set of negative sample data obtained in the step 1), so that the improved saddle feature deep learning convolutional neural network model can automatically identify potential saddle points in DEM data, and then correcting the coordinates of the saddle points by using a position regression MLP network to determine initially selected saddle points;
4) and selecting a threshold with the recognition probability larger than a set threshold from the initially selected saddle points as a final saddle point, extracting the corrected coordinates of the final saddle point, and mapping the corrected coordinates of the final saddle point back to the original DEM to obtain a final saddle point identification map, thereby completing the recognition of the saddle point based on the deep convolutional neural network.
The specific operation of the step 1) is as follows:
11) downloading 1:100 million tiff format topographic data from an SRTM open source website, and then analyzing DEM elevation data from the topographic data;
12) observing the distribution condition of terrain elevation values in the DEM, and cutting off DEM mountain land data;
13) reading DEM mountain data, cutting out a complete saddle area according to morphological characteristics of a saddle, marking position coordinates of a saddle point, and storing the position coordinates in an excel format acceptable by a deep neural network to form a training sample set.
The specific operation of step 2) is as follows:
the improved saddle feature deep learning convolutional neural network model comprises 1 input layer, 2 convolutional layers, 2 sampling layers, 2 full-link layers and 1 Softmax classification layer, wherein the input layer, the convolutional layers, the sampling layers and the full-link layers adopt correction linear units as activation functions, and weight parameters and bias parameters of the convolutional layers, the sampling layers and the full-link layers are initialized through a pre-trained Lenet-5 network model;
inputting DEM data into the improved saddle feature deep learning convolution neural network model directly, wherein the convolution feature map k of the i-th layer i As shown in formula (1):
wherein S is x And S y Representing the length and width, k, of the convolution kernel S i-1 Represents the input of the ith convolutional layer, w jn For weights learned in the convolution kernel, b i For the bias of the i-th filter, σ (-) is an activation function, and σ (-) adopts a ReLU function, which is expressed as:
the improved pooling layer of the saddle feature deep learning convolutional neural network model adopts max posing, and the output of the pooling layer is as follows:
wherein R is a sampling scale;
the convolutional layer and the pooling layer form a layered saddle point feature extractor, and excavation and learning of shallow and deep features of a saddle region are realized through multilayer superposition;
the output of the pooling layer is taken as the input of the full-link layer, and the output of the full-link layer is as follows:
wherein ReLU is chosen as the activation function σ (-),
is the weight between the ith node on level l-1 and the jth node on level l,
is the current bias;
the softmax layer outputs the recognition probability P (c | P) that the DEM lattice point is a potential saddle point, wherein,
where c is the target class, L is the index of the last layer, N C Is the number of targets.
The specific operation of the step 3) is as follows:
in order to enable the network to reach a stable state in the training process, the back propagation process of the network needs to be analyzed, wherein the back propagation process comprises a weight updating mode, a loss function expression mode and optimization algorithm selection for controlling gradient descent;
the backward propagation starts from the fully connected layer, and the operation shown in formula (6) is performed:
wherein E is a loss function,
is the first layer
Unit to l +1 th layer
The weight of the cell is such that,
is the first layer
Unit to l +1 th layer
Inputting the total unit;
in reverse propagation, the convolutional layer weight is adjusted by equation (7),
wherein the content of the first and second substances,
in order to be a non-linear mapping function,
is equal to
Is equal to
Repeat (R) toCarrying out forward and backward propagation processes until the deviation between the prediction result and the real result reaches the minimum;
performing L2 regularization operation on the weight of the full connection layer, namely adding lambda sigma to the original loss function for the full connection relation of the full connection layer w w 2 Punishment item to reduce the influence of noise in the training data on the network fitting process, wherein for the fully-connected layer with weight w, the expression of the loss function E is as follows:
wherein, E 0 For the non-regularized loss function, λ is the weighted decay factor, 1- η λ is the weighted decay factor, w i Is the learning rate.
The invention has the following beneficial effects:
the saddle point identification method based on the deep convolutional neural network comprises the steps of firstly constructing a training sample set, then analyzing the spatial morphological structure of a saddle, establishing an improved saddle characteristic deep learning convolutional neural network model, then training the improved saddle characteristic deep learning convolutional neural network model by using the training sample set, correcting the coordinates of the initially selected saddle point by using a position regression MLP network, and finally mapping the corrected saddle coordinate to an original DEM to obtain a saddle point identification map.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of an improved saddle feature deep learning convolutional neural network model in accordance with the present invention;
FIG. 3 is a diagram of a saddle point position regression network MLP according to the present invention;
FIG. 4a is a three-dimensional panoramic view of test data I of the present invention;
FIG. 4b is a point diagram of a manually calibrated saddle for test data I of the present invention;
FIG. 4c is a graph of saddle point identification results on test data I in accordance with the present invention;
FIG. 4d is a diagram showing the recognition result of the test data I in the "saddle point rapid extraction using topological relation" method according to the present invention;
FIG. 5a is a three-dimensional panoramic view of test data II;
FIG. 5b is a diagram of a saddle point manually calibrated for test data II;
FIG. 5c is a graph of saddle point identification results on test data II in accordance with the present invention;
FIG. 5d is a diagram showing the recognition result of the test data II in the "method for rapidly extracting saddle points using topological relations";
FIG. 6a is a three-dimensional panoramic view of test data III;
FIG. 6b is a saddle point diagram manually calibrated for test data III;
FIG. 6c is a diagram of the automatic saddle point identification results on data III of the present invention.
FIG. 6d is a diagram showing the recognition result of the test data III in the "saddle point rapid extraction using topological relation";
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, the saddle point identification method based on the deep convolutional neural network according to the present invention includes the following steps:
1) constructing a training sample set, wherein the training sample set is formed by m + n DEM mountain data, m is equal to n, and n is a positive sample data set A including a saddle region, and is equal to { A ═ A 1 ，A 2 ，A 3 ，…，A n M is a negative sample data set B not including the saddle region { B ═ B 1 ，B 2 ，B 3 ，…，B m Rectangular cutting is carried out on DEM sample data, coordinates of saddle points in the positive sample are marked, and positive and negative sample data are divided into a training set and a verification set according to a set proportion;
the specific operation of the step 1) is as follows:
11) downloading 1:100 million tiff format topographic data from SRTM open source website, and then analyzing DEM elevation data;
12) observing the distribution condition of terrain elevation values in the DEM, and cutting off DEM mountain land data;
13) reading DEM mountain data, cutting out a complete saddle area according to morphological characteristics of a saddle, marking position coordinates of a saddle point, and storing the position coordinates in an excel format acceptable by a deep neural network to form a training sample set.
Specifically, downloading topographic data in a 1:100 million tiff format from an SRTM open source website, analyzing DEM elevation data from the topographic data, and converting the DEM elevation data into a txt text format;
reading in data in a txt format, rendering terrain data into a gray graph according to the height value, selecting a highlight area in the gray graph, and cutting a 64 multiplied by 64 grid to obtain a DEM data block representing a mountain land;
reading in a DEM mountain land data block, cutting out a complete saddle region of a 6 multiplied by 6 grid according to morphological characteristics of a saddle, marking coordinates of saddle points, and storing the saddle points into an excel format acceptable by a deep neural network to form a sample data set;
writing a training data generator, wherein the training data generator takes a 6 multiplied by 6 marked saddle area as a center, selecting 6 multiplied by 6 DEM subgraphs around the DEM, namely cutting out 9 subgraphs of 6 multiplied by 6 from each DEM mountain data block, selecting 1 subgraph containing a saddle point at the center as a positive sample, traversing the rest 8 peripheral subgraphs not containing the saddle point, discarding the subgraphs not meeting the size of 6 multiplied by 6, and randomly selecting one from the rest subgraphs as a negative sample;
2) analyzing the spatial morphological structure of the saddle, and establishing an improved saddle feature deep learning convolutional neural network model;
the specific operation of the step 2) is as follows:
the improved saddle feature deep learning convolutional neural network model comprises 1 input layer, 2 convolutional layers, 2 sampling layers, 2 full-connection layers and 1 Softmax classification layer, wherein the input layer, the convolutional layers, the sampling layers and the full-connection layers adopt correction linear units as activation functions, and weight parameters and bias parameters of the convolutional layers, the sampling layers and the full-connection layers are initialized through a pre-trained Lenet-5 network model;
inputting DEM data into the improved saddle feature deep learning convolution neural network model directly, wherein the convolution feature map k of the i-th layer i As shown in formula (1):
wherein S is x And S y Representing the length and width, k, of the convolution kernel S i-1 Represents the input of the ith convolution layer, w jn For weights learned in the convolution kernel, b i For biasing of the i-th filter, σ () is an activation function, σ () uses a ReLU function, and the expression of the ReLU function is:
the improved pooling layer of the saddle feature deep learning convolutional neural network model adopts max posing, and the output of the pooling layer is as follows:
wherein R is a sampling scale;
the convolutional layer and the pooling layer form a layered saddle point feature extractor, and excavation and learning of shallow and deep features of a saddle region are realized through multilayer superposition;
the output of the pooling layer is taken as the input of the full-link layer, and the output of the full-link layer is as follows:
wherein ReLU is chosen as the activation function σ (-),
is the weight between the ith node on level l-1 and the jth node on level l,
is the current bias;
the softmax layer outputs the recognition probability P (c | P) that the DEM lattice point is a potential saddle point, wherein,
where c is the target class, L is the index of the last layer, N C Is the number of targets.
3) Setting various parameters required by training an improved saddle feature deep learning convolutional neural network model, wherein the various parameters comprise a target function, an optimization algorithm and a loss function, and pre-training the Lenet-5 network model by means of an ImageNet data set until the output of the Lenet-5 network model is optimal and reaches a stable state; initializing an improved saddle feature deep learning convolutional neural network model by using a pre-trained Lenet-5 network model, and then training the improved saddle feature deep learning convolutional neural network model by using the training set of positive sample data and the training set of negative sample data obtained in the step 1), so that the improved saddle feature deep learning convolutional neural network model can automatically identify potential saddle points in DEM data, and then correcting the coordinates of the saddle points by using a position regression MLP network to determine initially selected saddle points;
4) and selecting a threshold with the recognition probability larger than a set threshold from the initially selected saddle points as a final saddle point, extracting the corrected coordinates of the final saddle point, and mapping the corrected coordinates of the final saddle point back to the original DEM to obtain a final saddle point identification map, thereby completing the recognition of the saddle point based on the deep convolutional neural network.
The specific operation of the step 3) is as follows:
in order to enable the network to reach a stable state in the training process, the back propagation process of the network needs to be analyzed, wherein the back propagation process comprises a weight updating mode, a loss function expression mode and optimization algorithm selection for controlling gradient descent;
the backward propagation starts from the fully connected layer, and the operation shown in formula (6) is performed:
wherein E is a loss function,
is the first layer
Unit to l +1 th layer
The weight of the cell is such that,
is the first layer
Unit to l +1 th layer
Inputting the unit total;
in the reverse direction, the convolutional layer weight is adjusted by the equation (7),
wherein the content of the first and second substances,
in order to be a non-linear mapping function,
is equal to
Is equal to
Repeating the forward and backward propagation processes until the deviation between the predicted result and the real result is minimum;
performing L2 regularization operation on the weight of the full connection layer, namely adding lambda sigma to the original loss function for the full connection relation of the full connection layer w w 2 Punishment item to reduce the influence of noise in the training data on the network fitting process, wherein for the fully-connected layer with weight w, the expression of the loss function E is as follows:
wherein, E 0 For an irregular loss function, λ is the weight attenuation coefficient, 1- η λ is the weight attenuation factor, w i Is the learning rate.
Pre-training a Lenet-5 network model by means of an ImageNet data set until the output of the Lenet-5 network model is optimal and reaches a stable state, dividing a self-built positive sample set and a self-built negative sample set into a training set and a verification set, traversing the training set according to a specified batch size (batch _ size is 32), training an improved saddle feature deep learning convolutional neural network model, and completing the training of the improved saddle feature deep learning convolutional neural network model by setting training times, configuring a back propagation optimizer and the like, wherein the specific implementation of a training part is as shown in a Python program:
when a test set is high in test accuracy rate and a network is stable in an improved saddle feature deep learning convolutional neural network model, training a saddle coordinate correction part, firstly defining a saddle point coordinate regression network model MLP, wherein the structure is shown in FIG. 3, freezing parameters of each layer of CNN, only training the MLP network part, setting initial parameters of the MLP, selecting an Adam optimization algorithm and a mean square error as target functions, taking deviation change between saddle point prediction and a real position in the network training and testing process as a reference standard, and judging whether the network is stable, wherein a Python program of the MLP network training is as follows:
in the training process of the complete network, the model parameters are set as in table 1:
TABLE 1
The diversity and complexity of landforms are key factors influencing accurate extraction of topographic elements, for example, China is taken as an example, China is low and high in the west of the terrain, the altitude from the west to the east is in a descending trend, China is divided into three steps according to the altitude change of the terrain and the terrain, the first step is a Qinghai-Tibet plateau area, the average altitude H is larger than or equal to 4500m, the Qinghai-Tibet plateau area is taken as a starting point and is extended to Taihang mountain and Wushan to form a second step with the most abundant landform types in China, the average altitude is more than or equal to 1000 and 2000 meters, the third step is mainly based on hills, low mountains and plain, the average is more than 500m, and in order to verify the effectiveness of the invention in identifying points in different terrains, low mountains, medium and low mountains and high mountains data blocks I, II and III on three steps in 1:100 million SRDEM-DEM are selected for automatic identification testing.
TABLE 2
The three-dimensional panoramas of the three test data are shown in fig. 4a, 5a and 6a, respectively, and the manual saddle point labeling results on the 3 data are shown in fig. 4b, 5b and 6b as reference standards. The automatic labeling result of the present invention is shown in fig. 4c, fig. 5c and fig. 6c, wherein the red dots are saddle points extracted by the network. In contrast, the "saddle point fast extraction using topological relationships" is used to perform recognition, and the results are shown in fig. 4d, 5d, and 6d, where the black dots are the extracted saddle points.
For clarity, the labeling result chart of the present invention marks that the correctly extracted saddle is the type a point, the incorrectly extracted saddle is the type B point, and the missed saddle is the type C point. According to experimental results, the saddle points marked by the method have fewer missed extraction and false extraction numbers, and the identified saddle points mostly accord with qualitative description of a saddle region and are located in an intersection line region between two mountain tops and between a ridge line and a valley line. Even if the saddle point is mistakenly extracted by the invention, the surrounding situation of the saddle point also conforms to the morphological characteristics of the saddle area, only a little deviation occurs during accurate positioning, and the deep network is learned by taking the morphology and the spatial structure presented by the saddle area as the main characteristics. The deep neural network trained by the invention only uses 6000 saddle sample data; a small amount of saddle points are missed, because the network training data volume is unknown, the cognition of the unknown saddle region situation change is limited, and the phenomena can be compensated by increasing the number of training samples.
Claims (2)
1. A saddle point identification method based on a deep convolutional neural network is characterized by comprising the following steps:
1) constructing a training sample set which is formed by m + n DEM mountain data, wherein m is n, and n is a positive sample data set A which contains a saddle region A which is { A ═ A 1 ，A 2 ，A 3 ，…，A n M is a negative sample data set B not including the saddle region { B ═ B 1 ，B 2 ，B 3 ，…，B m Rectangular cutting is carried out on DEM sample data, coordinates of saddle points in the positive sample are marked, and positive and negative sample data are divided into a training set and a verification set according to a set proportion;
2) analyzing the spatial morphological structure of the saddle, and establishing an improved saddle feature deep learning convolutional neural network model;
3) setting various parameters required by training an improved saddle feature deep learning convolutional neural network model, wherein the various parameters comprise a target function, an optimization algorithm and a loss function, and pre-training the Lenet-5 network model by means of an ImageNet data set until the output of the Lenet-5 network model is optimal and reaches a stable state; initializing an improved saddle feature deep learning convolutional neural network model by using a pre-trained Lenet-5 network model, and then training the improved saddle feature deep learning convolutional neural network model by using the training set of positive sample data and the training set of negative sample data obtained in the step 1), so that the improved saddle feature deep learning convolutional neural network model can automatically identify potential saddle points in DEM data, and then correcting the coordinates of the potential saddle points through a position regression MLP network to determine initially selected saddle points;
4) selecting a threshold with the recognition probability larger than a set threshold from the initially selected saddle points as a final saddle point, extracting the corrected coordinates of the final saddle point, and mapping the corrected coordinates of the final saddle point back to the original DEM to obtain a final saddle point identifier map, thereby completing the saddle point recognition based on the deep convolutional neural network;
the specific operation of the step 2) is as follows:
the improved saddle feature deep learning convolutional neural network model comprises 1 input layer, 2 convolutional layers, 2 sampling layers, 2 full-link layers and 1 Softmax classification layer, wherein the input layer, the convolutional layers, the sampling layers and the full-link layers adopt correction linear units as activation functions, and weight parameters and bias parameters of the convolutional layers, the sampling layers and the full-link layers are initialized through a pre-trained Lenet-5 network model;
inputting DEM data into an improved saddle feature deep learning convolutional neural network model directly, wherein the convolutional feature map k of the ith layer i As shown in formula (1):
wherein S is x And S y Representing the length and width, k, of the convolution kernel S i-1 Represents the input of the ith convolutional layer, w jn For weights learned in the convolution kernel, b i For biasing of the i-th filter, σ () is an activation function, σ () uses a ReLU function, and the expression of the ReLU function is:
the improved pooling layer of the saddle feature deep learning convolutional neural network model adopts max posing, and the output of the pooling layer is as follows:
wherein R is a sampling scale;
the convolutional layer and the pooling layer form a layered saddle point feature extractor, and excavation and learning of shallow and deep features of a saddle region are realized through multilayer superposition;
the output of the pooling layer is taken as the input of the full-link layer, and the output of the full-link layer is as follows:
wherein ReLU is chosen as the activation function σ (-),
is the weight between the ith node on level l-1 and the jth node on level l,
is the current bias;
the softmax layer outputs the recognition probability P (c | P) that a DEM grid point is a potential saddle point, wherein,
where c is the target class, L is the index of the last layer, N C Is the number of targets.
2. The saddle point identification method based on the deep convolutional neural network according to claim 1, wherein the specific operation of step 1) is:
11) downloading 1:100 million tiff format topographic data from SRTM open source website, and then analyzing DEM elevation data;
12) observing the distribution condition of terrain elevation values in the DEM, and cutting off DEM mountain land data;
13) reading DEM mountain data, cutting out a complete saddle area according to morphological characteristics of a saddle, marking position coordinates of a saddle point, and storing the position coordinates in an excel format acceptable by a deep neural network to form a training sample set.
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| CN108510467B (en) * | 2018-03-28 | 2022-04-08 | 西安电子科技大学 | SAR image target identification method based on depth deformable convolution neural network |
| CN109145171B (en) * | 2018-07-23 | 2020-09-08 | 广州市城市规划勘测设计研究院 | Multi-scale map data updating method |
| CN109739942B (en) * | 2018-12-14 | 2020-10-16 | 南京泛在地理信息产业研究院有限公司 | Saddle point extraction method based on contour line model |
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