CN116167887A - Strip mine mining planning method and device based on remote sensing image feature point detection - Google Patents
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Abstract
The application provides a strip mine mining planning method and device based on remote sensing image feature point detection, wherein the method comprises the following steps: acquiring a first strip mine remote sensing image and a second strip mine remote sensing image; obtaining an absolute value of a difference value of a pixel spectrum value at a corresponding position between the first strip mine remote sensing image and the second strip mine remote sensing image according to the first strip mine remote sensing image and the second strip mine remote sensing image, and constructing a differential image; detecting SUFF feature points of the first strip mine remote sensing image and SUFF feature points of the second strip mine remote sensing image, and obtaining a first feature point set of the first strip mine remote sensing image and a second feature point set of the second strip mine remote sensing image; obtaining a change detection result according to the first characteristic point set, the second characteristic point set and the differential image; and carrying out mining planning on the strip mine based on the change detection result. The method and the device acquire accurate change detection results based on the remote sensing images and provide a data basis for strip mine mining planning.
Description
Technical Field
The application relates to the technical field of intelligent mines, in particular to a strip mine exploitation planning method and device based on remote sensing image feature point detection.
Background
The remote sensing image change detection of the mining area is to analyze remote sensing images of the same target or region in different periods to acquire change information. With the development of space remote sensing technology, remote sensing change detection is widely applied to the field of intelligent mines, and plays an important role in promoting planned exploitation and environmental protection construction. Therefore, how to obtain effective and accurate change information according to the strip mine remote sensing image so as to carry out mining planning or disaster early warning monitoring on the strip mine becomes one of the important research directions in the field.
Disclosure of Invention
The present application aims to solve, at least to some extent, one of the technical problems in the related art.
For this reason, the first aspect of the present application proposes a strip mine mining planning method based on remote sensing image feature point detection, including:
acquiring a first strip mine remote sensing image and a second strip mine remote sensing image;
obtaining an absolute value of a difference value of pixel spectrum values at a corresponding position between the first strip mine remote sensing image and the second strip mine remote sensing image according to the first strip mine remote sensing image and the second strip mine remote sensing image, and constructing a differential image;
detecting SUFF feature points of the first strip mine remote sensing image and SUFF feature points of the second strip mine remote sensing image, and obtaining a first feature point set of the first strip mine remote sensing image and a second feature point set of the second strip mine remote sensing image;
obtaining a change detection result according to the first characteristic point set, the second characteristic point set and the differential image;
and carrying out mining planning on the strip mine based on the change detection result.
In some embodiments of the present application, the obtaining a change detection result according to the first feature point set, the second feature point set, and the differential image includes: obtaining a variable pixel sample and a non-variable pixel sample according to the characteristic points in the first characteristic point set and the second characteristic point set; training a support vector machine classification model according to the change type pixel sample and the non-change type pixel sample; and classifying the differential image through the support vector machine classification model to obtain the change detection result.
In some embodiments of the present application, the change detection result includes at least one of a heading line change detection result, a stack change detection result, and a climbing line change detection result.
In some embodiments of the present application, the first strip mine remote sensing image and the second strip mine remote sensing image are the preprocessed first strip mine remote sensing image and the preprocessed second strip mine remote sensing image; wherein the preprocessing comprises spatial registration and/or radiation correction.
In some embodiments of the present application, the mining planning of the strip mine based on the change detection result includes: obtaining mining planning information according to the pixel quantity of the change area and the pixel quantity of the non-change area in the change detection result; and carrying out mining planning on the strip mine according to the mining planning information.
The second aspect of the present application proposes a strip mine mining planning device based on remote sensing image feature point detection, including:
the first acquisition module is used for acquiring a first strip mine remote sensing image and a second strip mine remote sensing image;
the second acquisition module is used for acquiring an absolute value of a pixel spectrum value difference value at a corresponding position between the first strip mine remote sensing image and the second strip mine remote sensing image according to the first strip mine remote sensing image and the second strip mine remote sensing image, and constructing a difference image;
the characteristic point detection module is used for detecting SUFF characteristic points of the first strip mine remote sensing image and SUFF characteristic points of the second strip mine remote sensing image, and obtaining a first characteristic point set of the first strip mine remote sensing image and a second characteristic point set of the second strip mine remote sensing image;
the third acquisition module is used for acquiring a change detection result according to the first characteristic point set, the second characteristic point set and the differential image;
and the mining planning module is used for carrying out mining planning on the strip mine based on the change detection result.
In some embodiments of the present application, the third obtaining module is specifically configured to: obtaining a variable pixel sample and a non-variable pixel sample according to the characteristic points in the first characteristic point set and the second characteristic point set; training a support vector machine classification model according to the change type pixel sample and the non-change type pixel sample; and classifying the differential image through the support vector machine classification model to obtain the change detection result.
In some embodiments of the present application, the production planning module is specifically configured to: obtaining mining planning information according to the pixel quantity of the change area and the pixel quantity of the non-change area in the change detection result; and carrying out mining planning on the strip mine according to the mining planning information.
A third aspect of the present application proposes an electronic device comprising: a processor; and a memory for storing the processor-executable instructions; wherein the instructions are executable by the processor to enable the processor to perform the method of the first aspect.
A fourth aspect of the present application proposes a non-transitory computer readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform the method of the first aspect.
According to the strip mine mining planning method based on remote sensing image feature point detection, as the SURF feature points are local expressions of image features, the local specificity of the image can be reflected well, and the method has good robustness to image noise. The method and the device have stronger application value in mining planning and disaster early warning in the field of intelligent mines through the change detection of the characteristic points. The method can effectively overcome adverse effects of noise on the change detection precision, is favorable for processing remote sensing images with complex backgrounds, improves the precision of change detection, and provides a data base for strip mine mining planning.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic flow chart of a strip mine mining planning method based on remote sensing image feature point detection according to an embodiment of the present application;
fig. 2 is a flowchart of a method for obtaining a change detection result according to a first feature point set, a second feature point set and a differential image provided in an embodiment of the present application;
fig. 3 is a structural block diagram of a strip mine mining planning device based on remote sensing image feature point detection according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The application provides a strip mine mining planning method and device based on remote sensing image feature point detection. Specifically, a strip mine mining planning method and device based on remote sensing image feature point detection according to the embodiments of the present application are described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a strip mine mining planning method based on remote sensing image feature point detection according to an embodiment of the present application. As shown in fig. 1, the strip mine mining planning method based on the remote sensing image feature point detection can include, but is not limited to, the following steps.
And step 101, acquiring a first strip mine remote sensing image and a second strip mine remote sensing image.
The first strip mine remote sensing image and the second strip mine remote sensing image are remote sensing images of the same strip mine at different times. Optionally, in some embodiments of the present application, the first and second strip mine remote sensing images are preprocessed first and second strip mine remote sensing images. Wherein the preprocessing may include spatial registration and/or radiation correction.
And 102, obtaining absolute values of pixel spectrum value differences at corresponding positions between the first strip mine remote sensing image and the second strip mine remote sensing image according to the first strip mine remote sensing image and the second strip mine remote sensing image, and constructing a differential image.
And step 103, detecting SUFF feature points of the first strip mine remote sensing image and SUFF feature points of the second strip mine remote sensing image, and obtaining a first feature point set of the first strip mine remote sensing image and a second feature point set of the second strip mine remote sensing image.
And 104, obtaining a change detection result according to the first characteristic point set, the second characteristic point set and the differential image.
In some embodiments of the present application, the variable pixel sample and the non-variable pixel sample may be obtained according to the feature points in the first feature point set and the second feature point set. And further training a support vector machine classification model. And classifying the differential image through the support vector machine classification model, thereby obtaining a change detection result.
Alternatively, in some embodiments of the present application, the change detection result may be a pixel amount of the change region and a pixel amount of the non-change region, which are further analyzed, so that at least one of a tunneling line change detection result, a stack change detection result, and a climbing line change detection result may be obtained.
And 105, mining planning is carried out on the strip mine based on the change detection result.
As one possible implementation, the mining planning information may be obtained according to the pixel amount of the changed region and the pixel amount of the non-changed region in the change detection result. And carrying out mining planning on the strip mine according to the mining planning information.
As one example, the remaining producible time of the strip mine can be predictive-analyzed based on the pixel amount of the change region and the pixel amount of the non-change region in the change detection result.
According to the strip mine mining planning method based on remote sensing image feature point detection, as the SURF feature points are local expressions of image features, the local specificity of the image can be reflected well, and the method has good robustness to image noise. The method and the device have stronger application value in mining planning and disaster early warning in the field of intelligent mines through the change detection of the characteristic points. The method can effectively overcome adverse effects of noise on the change detection precision, is favorable for processing remote sensing images with complex backgrounds, improves the precision of change detection, and provides a data base for strip mine mining planning.
Fig. 2 is a flowchart of a method for obtaining a change detection result according to a first feature point set, a second feature point set and a difference image according to an embodiment of the present application. As shown in fig. 2, the method may include, but is not limited to, the following steps.
As a possible implementation manner, the feature points in the first feature point set and the second feature point set may be matched, so as to obtain feature points matched with each other between the first feature point set and the second feature point set, and feature points which cannot be matched with each other. And calculating brightness values of pixels at corresponding positions of the feature points which are matched with each other in the differential image, obtaining non-changing pixel samples and classifying the non-changing pixel samples into a non-changing pixel sample set. For the followingAnd combining the feature points which cannot be matched with each other in the first feature point set and the second feature point set into a non-matching set T, calculating the brightness value of the pixel of each pixel point in the non-matching set T at the corresponding position in the differential image, and classifying the brightness value into a first set G. Assuming that the pixels in the non-matching set T are composed of non-changed, unlabeled and changed pixels, and the brightness of each pixel is subject to Gaussian distribution and is respectively marked as N (g|mu 1 ,δ 1 2 )、N(g|μ 2 ,δ 2 2 )、N(g|μ 3 ,δ 3 2 ) The overall luminance histogram should be a three-dimensional gaussian mixture distribution:
Z(g)＝w 1 N(g|μ 1 ,δ 1 2 )+w 2 N(g|μ 2 ,δ 2 2 )+w 3 N(g|μ 3 ,δ 3 2 )
wherein Z (G) represents three-dimensional Gaussian mixture distribution, and G epsilon G is a pixel brightness value; w (w) 1 、w 2 W 3 The method comprises the steps of carrying out a first treatment on the surface of the Weights of non-variable pixels, non-marked pixels and variable pixels are distributed in the Gaussian mixture; n (g|mu) 1 ,δ 1 2 ) Representing the luminance compliance mean value of non-varying class pixels as mu 1 The variance is delta 1 2 Is a gaussian distribution of (c); n (g|mu) 2 ,δ 2 2 ) Representing unlabeled class pixels with luminance compliance mean μ 2 Variance is delta 2 2 Is a gaussian distribution of (c); n (g|mu) 3 ,δ3 2 ) Representing the luminance compliance mean value of the variable class pixel as mu 3 Variance is delta 3 2 Is a gaussian distribution of (c).
Solving the related parameters by using an EM algorithm, and finally classifying the pixel brightness values conforming to the following formula in the first set G into a variable pixel sample set S C 。
S C = { g|g∈g and G is not less than μ 3 -3δ 3 }
μ 3 、δ 3 The mean and standard deviation of the gaussian distribution of varying pixel-like intensities, respectively.
And constructing a training set and a testing set according to the variable pixel samples and the non-variable pixel samples. And reconstructing corresponding training set labels and test set labels, wherein the elements in the training set labels are all 0 and equal to the training set in size, and the elements in the test set labels are all 1 and equal to the test set in size. And finally training a support vector machine classification model on the basis of preprocessing the test set and the training set.
And 203, classifying the differential image through a support vector machine classification model to obtain a change detection result.
And classifying the differential image through a support vector machine classification model to obtain a change region and a non-change region, wherein a final change detection result is embodied in the form of a binary change detection mask, wherein a pixel with a value of 0 represents a non-change category, and a pixel with a value of 1 represents a change category.
By implementing the embodiment of the disclosure, the first characteristic point set, the second characteristic point set and the differential image are correspondingly processed, so that an accurate change detection result can be obtained, and a data basis is provided for mining planning of the strip mine.
Fig. 3 is a structural block diagram of a strip mine mining planning device based on remote sensing image feature point detection according to an embodiment of the present application. As shown in fig. 3, the strip mine mining planning device based on remote sensing image feature point detection includes: a first acquisition module 301, a second acquisition module 302, a feature point detection module 303, a third acquisition module 304, and a production planning module 305. Wherein,,
the first acquiring module 301 is configured to acquire a first strip mine remote sensing image and a second strip mine remote sensing image.
The second obtaining module 302 is configured to obtain, according to the first strip mine remote sensing image and the second strip mine remote sensing image, an absolute value of a difference value of a pixel spectrum value at a corresponding position between the first strip mine remote sensing image and the second strip mine remote sensing image, and construct a differential image.
The feature point detection module 303 is configured to detect a SUFF feature point of the first strip mine remote sensing image and a SUFF feature point of the second strip mine remote sensing image, and obtain a first feature point set of the first strip mine remote sensing image and a second feature point set of the second strip mine remote sensing image.
The third obtaining module 304 is configured to obtain a change detection result according to the first feature point set, the second feature point set, and the differential image.
The mining planning module 305 is configured to perform mining planning on the strip mine based on the change detection result.
In some embodiments of the present application, the third obtaining module 304 is specifically configured to: obtaining a variable pixel sample and a non-variable pixel sample according to the characteristic points in the first characteristic point set and the second characteristic point set; training a support vector machine classification model according to the change type pixel sample and the non-change type pixel sample; and classifying the differential image through the support vector machine classification model to obtain the change detection result.
In some embodiments of the present application, the production planning module 305 is specifically configured to: obtaining mining planning information according to the pixel quantity of the change area and the pixel quantity of the non-change area in the change detection result; and carrying out mining planning on the strip mine according to the mining planning information.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
According to the strip mine mining planning device based on remote sensing image feature point detection, as the SURF feature points are local expressions of image features, the local specificity of the image can be reflected well, and the strip mine mining planning device has good robustness to image noise. The method and the device have stronger application value in mining planning and disaster early warning in the field of intelligent mines through the change detection of the characteristic points. The method can effectively overcome adverse effects of noise on the change detection precision, is favorable for processing remote sensing images with complex backgrounds, improves the precision of change detection, and provides a data base for strip mine mining planning.
In order to achieve the above embodiments, the present application further proposes an electronic device including: a processor, and a memory for storing instructions executable by the processor. The instructions are executed by the processor to enable the processor to execute the strip mine mining planning method based on the remote sensing image feature point detection.
In order to implement the above embodiment, the present application further proposes a non-transitory computer readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the aforementioned strip mine mining planning method based on remote sensing image feature point detection.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. The strip mine mining planning method based on remote sensing image feature point detection is characterized by comprising the following steps of:
acquiring a first strip mine remote sensing image and a second strip mine remote sensing image;
obtaining an absolute value of a difference value of pixel spectrum values at a corresponding position between the first strip mine remote sensing image and the second strip mine remote sensing image according to the first strip mine remote sensing image and the second strip mine remote sensing image, and constructing a differential image;
detecting SUFF feature points of the first strip mine remote sensing image and SUFF feature points of the second strip mine remote sensing image, and obtaining a first feature point set of the first strip mine remote sensing image and a second feature point set of the second strip mine remote sensing image;
obtaining a change detection result according to the first characteristic point set, the second characteristic point set and the differential image;
and carrying out mining planning on the strip mine based on the change detection result.
2. The method of claim 1, wherein the obtaining a change detection result from the first set of feature points, the second set of feature points, and the difference image comprises:
obtaining a variable pixel sample and a non-variable pixel sample according to the characteristic points in the first characteristic point set and the second characteristic point set;
training a support vector machine classification model according to the change type pixel sample and the non-change type pixel sample;
and classifying the differential image through the support vector machine classification model to obtain the change detection result.
3. The method of claim 1, wherein the change detection results comprise at least one of a heading line change detection result, a stack change detection result, and a climbing line change detection result.
4. The method of claim 1, wherein the first and second strip mine remote sensing images are the preprocessed first and second strip mine remote sensing images; wherein the preprocessing comprises spatial registration and/or radiation correction.
5. The method of claim 1, wherein the mining planning of the strip mine based on the change detection results comprises:
obtaining mining planning information according to the pixel quantity of the change area and the pixel quantity of the non-change area in the change detection result;
and carrying out mining planning on the strip mine according to the mining planning information.
6. Strip mine exploitation planning device based on remote sensing image feature point detection, characterized by comprising:
the first acquisition module is used for acquiring a first strip mine remote sensing image and a second strip mine remote sensing image;
the second acquisition module is used for acquiring an absolute value of a pixel spectrum value difference value at a corresponding position between the first strip mine remote sensing image and the second strip mine remote sensing image according to the first strip mine remote sensing image and the second strip mine remote sensing image, and constructing a difference image;
the characteristic point detection module is used for detecting SUFF characteristic points of the first strip mine remote sensing image and SUFF characteristic points of the second strip mine remote sensing image, and obtaining a first characteristic point set of the first strip mine remote sensing image and a second characteristic point set of the second strip mine remote sensing image;
the third acquisition module is used for acquiring a change detection result according to the first characteristic point set, the second characteristic point set and the differential image;
and the mining planning module is used for carrying out mining planning on the strip mine based on the change detection result.
7. The apparatus of claim 6, wherein the third acquisition module is specifically configured to:
obtaining a variable pixel sample and a non-variable pixel sample according to the characteristic points in the first characteristic point set and the second characteristic point set;
training a support vector machine classification model according to the change type pixel sample and the non-change type pixel sample;
and classifying the differential image through the support vector machine classification model to obtain the change detection result.
8. The apparatus of claim 6, wherein the production planning module is specifically configured to:
obtaining mining planning information according to the pixel quantity of the change area and the pixel quantity of the non-change area in the change detection result;
and carrying out mining planning on the strip mine according to the mining planning information.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions; wherein the instructions are executable by the processor to enable the processor to perform the method of any one of claims 1-5.
10. A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of any one of claims 1-5.
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