CN111915625A - Energy integral remote sensing image terrain shadow automatic detection method and system - Google Patents

Energy integral remote sensing image terrain shadow automatic detection method and system Download PDF

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CN111915625A
CN111915625A CN202010815816.5A CN202010815816A CN111915625A CN 111915625 A CN111915625 A CN 111915625A CN 202010815816 A CN202010815816 A CN 202010815816A CN 111915625 A CN111915625 A CN 111915625A
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张利军
徐质彬
杨晓弘
杨海燕
尹展
曹创华
文春华
黄志飙
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Hunan Xinxiang Geophysical Exploration Engineering Co ltd
Research Institute Of Hunan Province Nonferrous Metals Geological Exploration Bureau
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Abstract

The invention discloses an energy-integrated remote sensing image terrain shadow automatic detection method and system, wherein the method comprises the following steps: acquiring remote sensing image data, and carrying out radiometric calibration on the acquired remote sensing image data to obtain a radiant energy spectrum curve; data cutting is carried out on the detection area to be detected, and related parameters of remote sensing image data wave bands are obtained; calculating integral energy according to the parameter information and the radiation energy spectrum curve; and (4) according to the statistical characteristic information of the integral energy gray-scale map, selecting a proper threshold value to perform segmentation and extraction on the shadow region. The method and the system for automatically detecting the terrain shadow of the remote sensing image of the energy integral have low calculation complexity, high efficiency and convenience; the automatic realization process is simple, and the detection precision is very high in a complicated terrain area.

Description

Energy integral remote sensing image terrain shadow automatic detection method and system
Technical Field
The invention relates to the field of terrain shadow detection, and particularly discloses an energy-integration remote-sensing image terrain shadow automatic detection method and system.
Background
Currently, algorithms for shadow detection can be roughly divided into two categories, model-based and shadow attribute feature-based. The model-based detection method is mainly characterized in that a geometric model is established according to the geometric property of light physical propagation and the prior knowledge of the environmental conditions such as the geometric shape of the ground feature, the solar altitude angle, the sensor parameters and the like to realize the detection and the segmentation of the shadow, and is also called as the geometric model. For example, the method based on the blackbody radiation model realizes the detection of the shadow region according to the blackbody radiation principle of different irradiation light sources and different color temperatures. The method has the disadvantages of poor applicability, large limitation, high calculation complexity, large calculation amount and different acquisition difficulties of the prior knowledge data of different scenes. The method based on the characteristic attribute generally adopts a threshold segmentation method to realize the detection of the shadow according to the difference between the shadow area and the non-shadow area in the characteristics of brightness, color, texture and the like. After the color space transformation model such as HSV, YIQ, YCbCr and the like is used for processing, the shadow area and the non-shadow area have separability on the color characteristics such as brightness, hue or saturation; and then, if the shadow sample in the image is selected manually for training, the attribute of the shadow is dynamically extracted to serve as a decision parameter for judging the shadow region, candidate shadow detection results based on the brightness space and the color space are obtained through contrast stretching of the brightness space and statistics of the Boolean relation of color components of the color space, the candidate shadow results of the two spaces are organically combined, and the shadow detection is realized through hard threshold segmentation. The method based on the characteristic attributes is relatively simple and convenient, but has poor universality and low robustness, and mainly because the remote sensing image scene is complex and the brightness change interval of a shadow area is large, the method is difficult to select the attribute which is stable to different remote sensing images. Particularly, for mountain remote sensing images with serious green vegetation coverage, a great interference is formed on a detection result due to the fact that features of a green land and a terrain shadow area are close, and how to avoid false detection of the green land and the like as the terrain shadow is also one of key problems to be solved in remote sensing image shadow detection.
In summary, the shortcomings of the current remote sensing image terrain shadow detection algorithm can be roughly summarized as the following four points:
1. the attributes of low-brightness ground objects such as green lands, water bodies and the like in non-urban areas are similar to those of shadows, so that false detection of the shadows can be caused;
2. the phenomenon of same object, different spectrum or same foreign object spectrum in the remote sensing image can cause the brightness variability of the shadow area to be increased, and the stable attribute suitable for different remote sensing images is difficult to select, so that missing detection or false detection is formed;
3. the terrain shadow generally has a transition region, and the accuracy of the traditional detection algorithm on the edge of the shadow is difficult to quantitatively control;
4. the detection method based on the geometric model usually needs to input more prior knowledge parameters, has higher parameter acquisition difficulty, needs more manual participation, and has lower automation degree and high algorithm complexity.
Therefore, the defects of the existing remote sensing image terrain shadow detection algorithm are a technical problem to be solved urgently.
Disclosure of Invention
The invention provides an energy-integration remote sensing image terrain shadow automatic detection method and system, and aims to solve the technical problem of the defects of the existing remote sensing image terrain shadow detection algorithm.
The invention relates to an energy-integrated remote sensing image terrain shadow automatic detection method, which comprises the following steps:
acquiring remote sensing image data, and carrying out radiometric calibration on the acquired remote sensing image data to obtain a radiant energy spectrum curve;
data cutting is carried out on the detection area to be detected, and the wave band information parameters of the remote sensing image are obtained;
and calculating integral energy according to the acquired remote sensing image wave band information parameters and the radiation energy spectrum curve.
Further, according to the obtained remote sensing image wave band information parameters and the radiation energy spectrum curve, the step of calculating the integral energy further comprises the following steps:
programming the calculated integral energy by using a graphic processing tool to obtain an energy integral gray image;
counting the acquired energy integral gray level image to acquire a shadow region segmentation threshold;
selecting a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integral gray level image;
and deriving the contour vector of the shadow partition area, and then finishing the automatic detection of the terrain shadow of the remote sensing image to be detected.
Further, the step of selecting a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integration gray level image and deriving a segmentation contour vector further comprises the following steps:
and if the selected threshold has an unsatisfactory shadow region segmentation effect, continuously adjusting the size of the threshold until the satisfactory segmentation effect is obtained, and completing automatic detection of the terrain shadow of the remote sensing image to be detected.
Further, the integrated energy is obtained by the following energy integration function expression:
Figure BDA0002630829320000031
the correspondence and form are as follows:
Figure BDA0002630829320000032
wherein λ isnRepresents the nth band spectral dimension; lambda [ alpha ]n+1Represents the n +1 band spectral dimension; λ represents the spectral dimension; r (lambda)n+1，xY) represents the radiation energy of the pixel element P (x, y)) as a function of the wavelength of the (n + 1) th band; r (lambda)nX, y) represents the radiation energy of the picture element P (x, y)) as a function of the wavelength of the nth wavelength band.
Further, the statistical characteristic of the energy integration gray scale image is asymmetric bimodal.
Further, the energy integration gray image comprises a left peak area and a right peak area, wherein the left peak area represents a shadow area image element set, and the right peak area represents a non-shadow area image element set; and the valley bottom between the left peak area and the right peak area is a shadow area segmentation threshold.
Another aspect of the invention relates to an energy-integrated remote-sensing image terrain shadow automatic detection system, which comprises:
the first acquisition module is used for acquiring remote sensing image data and carrying out radiometric calibration on the acquired remote sensing image data to obtain a radiant energy spectrum curve;
the second acquisition module is used for cutting data of the detection area to be detected and acquiring the wave band information parameters of the remote sensing image;
and the calculating module is used for calculating integral energy according to the acquired remote sensing image wave band information parameters and the radiation energy spectrum curve.
Further, the energy-integrated remote sensing image terrain shadow automatic detection system further comprises:
the third acquisition module is used for programming the calculated integral energy by utilizing a graphic processing tool to acquire an energy integral gray image;
the fourth acquisition module is used for counting the acquired energy integral gray level images and acquiring a shadow region segmentation threshold;
the segmentation module is used for selecting a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integral gray level image;
and the detection module is used for deriving the contour vector of the shadow partition area and then completing automatic detection of the terrain shadow of the remote sensing image to be detected.
Further, the energy-integrated remote sensing image terrain shadow automatic detection system further comprises:
and the execution module is used for continuously adjusting the size of the threshold value if the selected threshold value has an unsatisfactory shadow region segmentation effect until the ideal segmentation effect is obtained, and completing automatic detection of the terrain shadow of the remote sensing image to be detected.
Further, the integrated energy is obtained by the following energy integration function expression:
Figure BDA0002630829320000041
the correspondence and form are as follows:
Figure BDA0002630829320000042
wherein λ isnRepresents the nth band spectral dimension; lambda [ alpha ]n+1Represents the n +1 band spectral dimension; λ represents the spectral dimension; r (lambda)n+1X, y) represents the radiation energy of the pixel P (x, y)) as a function of the wavelength of the n +1 th band; r (lambda)nX, y) represents the radiation energy of the picture element P (x, y)) as a function of the wavelength of the nth wavelength band.
The beneficial effects obtained by the invention are as follows:
the invention provides an energy integral remote sensing image terrain shadow automatic detection method and system, which obtains remote sensing image data, and performs radiometric calibration on the obtained remote sensing image data to obtain a radiant energy spectrum curve; data cutting is carried out on the detection area to be detected, and the wave band information parameters of the remote sensing image are obtained; and calculating integral energy according to the acquired remote sensing image wave band information parameters and the radiation energy spectrum curve. The method and the system for automatically detecting the terrain shadow of the remote sensing image of the energy integral have low calculation complexity, high efficiency and convenience; the automatic realization process is simple, and the detection precision is very high in a complicated terrain area.
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Fig. 1 is a schematic flow chart of a first embodiment of a method for automatically detecting terrain shadow of energy-integrated remote sensing images according to the present invention;
FIG. 2 is a schematic flow chart of a method for automatically detecting terrain shadow in remote-sensing images by energy integration according to a second embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for automatically detecting terrain shadow in remote-sensing images by energy integration according to a third embodiment of the present invention;
FIG. 4 is an initial image of ASTER image terrain shadow detection in the method for automatically detecting energy-integrated terrain shadow of remote sensing image provided by the present invention;
FIG. 5 is an image of the detection result of ASTER image terrain shadow detection in the method for automatically detecting energy-integrated remote sensing image terrain shadow provided by the present invention;
FIG. 6 is a functional block diagram of a first embodiment of an energy-integrated remote-sensing image terrain shadow automatic detection system provided by the present invention;
FIG. 7 is a functional block diagram of a system for automatically detecting terrain shadow in remote sensing images by energy integration according to a second embodiment of the present invention;
fig. 8 is a functional block diagram of an energy-integrated remote sensing image terrain shadow automatic detection system according to a third embodiment of the present invention.
The reference numbers illustrate:
10. a first acquisition module; 20. a second acquisition module; 30. a calculation module; 40. a third obtaining module; 50. a fourth obtaining module; 60. a segmentation module; 70. a detection module; 80. and executing the module.
Detailed Description
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
As shown in fig. 1, a first embodiment of the present invention provides an energy-integrated method for automatically detecting a topographic shadow of a remote sensing image, including the following steps:
and S100, acquiring remote sensing image data, and carrying out radiometric calibration on the acquired remote sensing image data to obtain a radiant energy spectrum curve.
And acquiring ASTER remote sensing image data, and carrying out radiometric calibration on the acquired remote sensing image data to obtain a radiation energy spectrum curve r (lambda, x, y). Wherein the radiation energy spectral curve r (λ, x, y) represents the radiation energy of the pixel P (x, y) as a function of wavelength.
And S200, cutting data of the detection area to be detected to obtain the wave band information parameters of the remote sensing image.
And cutting data of the remote sensing image area to be detected of the ASTER remote sensing image to obtain the wave band information parameter of the remote sensing image. As shown in fig. 4, fig. 4 is an initial image for detecting the terrain shadow of the ASTER image in the method for automatically detecting the terrain shadow of the energy-integrated remote sensing image provided by the present invention, and the information parameters of the bands of the remote sensing image include the spectral dimension, the grayscale map of the single band of the ASTER remote sensing image data, and the band interval or distance between adjacent bands.
And step S300, calculating integral energy according to the acquired remote sensing image wave band information parameters and the radiation energy spectrum curve.
An energy integral function is defined according to the total energy of the radiation received by the shadow area and the non-shadow area, and the specific form is as follows:
Figure BDA0002630829320000061
in equation (1), E (x, y) represents the integrated energy of solar radiation received by pixel P (x, y), λ1Represents the 1 st band; lambda [ alpha ]nRepresents an nth band; r (λ, x, y) represents the radiant energy of the picture element P (x, y) as a function of wavelength.
The remote sensing image data is in a discrete form in a wave band dimension, and an integral equation is converted into an energy integral function expression. Calculating integral energy according to the obtained remote sensing image waveband information parameters and the radiation energy spectrum curve, wherein the integral energy is obtained through the following energy integral function expression:
Figure BDA0002630829320000062
in the formula (2), λnRepresents the nth band spectral dimension; lambda [ alpha ]n+1Represents the n +1 band spectral dimension; λ represents the spectral dimension; r (lambda)n+1X, y) represents the radiation energy of the pixel P (x, y)) as a function of the wavelength of the n +1 th band; r (lambda)nX, y) represents the radiation energy of the picture element P (x, y)) as a function of the wavelength of the nth wavelength band.
Compared with the prior art, the method for automatically detecting the topographic shadow of the remote sensing image based on the energy integration obtains the remote sensing image data, and performs radiometric calibration on the obtained remote sensing image data to obtain a radiant energy spectral curve; data cutting is carried out on the detection area to be detected, and the wave band information parameters of the remote sensing image are obtained; and calculating integral energy according to the acquired remote sensing image wave band information parameters and the radiation energy spectrum curve. The method for automatically detecting the terrain shadow of the remote sensing image based on the energy integration has the advantages of low calculation complexity, high efficiency and convenience; the automatic realization process is simple, and the detection precision is very high in a complicated terrain area.
Further, please refer to fig. 2, where fig. 2 is a schematic flow chart of a second embodiment of the energy-integrated remote sensing image terrain shadow automatic detection method provided by the present invention, and on the basis of the first embodiment, the present embodiment provides an energy-integrated remote sensing image terrain shadow automatic detection method, which further includes the steps of:
and S400, programming the calculated integral energy by using a graphic processing tool to obtain an energy integral gray image.
The calculated integrated energy E (x, y) was programmed using Band Math tool of ENVI5.3 to obtain an energy integrated gray scale image.
And S500, counting the acquired energy integral gray level image, and acquiring a shadow region segmentation threshold value.
The statistical characteristic is generally asymmetric double peak type, the left peak area represents a shadow area image element set, and the right peak area represents a non-shadow area image element set; the left peak is generally lower than the right peak. The trough bottom between the two peaks is the shadow segmentation threshold (multiple experiments).
And S600, selecting a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integral gray level image.
And selecting a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integral gray level image.
And S700, deriving the contour vector of the shadow partition area, and then finishing the automatic detection of the terrain shadow of the remote sensing image to be detected.
The derived segmentation contour vector is shown in fig. 5, fig. 5 is an image of an ASTER image terrain shadow detection result in the energy-integrated remote sensing image terrain shadow automatic detection method provided by the invention, and the detection result is a white contour line delineation part.
Compared with the prior art, the method for automatically detecting the topographic shadow of the remote sensing image of the energy integration, provided by the embodiment, has the advantages that the calculated integral energy is programmed by utilizing a graphic processing tool, and an energy integration gray image is obtained; counting the acquired energy integral gray level image to acquire a shadow region segmentation threshold; selecting a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integral gray level image; and deriving the contour vector of the shadow partition area, and then finishing the automatic detection of the terrain shadow of the remote sensing image to be detected. The method for automatically detecting the terrain shadow of the remote sensing image based on the energy integration has the advantages of low calculation complexity, high efficiency and convenience; the automatic realization process is simple, and the detection precision is very high in a complicated terrain area.
Preferably, referring to fig. 3, fig. 3 is a schematic flow chart of a third embodiment of the energy-integrated remote-sensing image terrain shadow automatic detection method provided by the present invention, on the basis of the second embodiment, after the step S600, the method further includes:
and step S700A, if the selected threshold is not ideal for the shadow region segmentation effect, continuing to adjust the threshold until the ideal segmentation effect is obtained, and completing the automatic detection of the terrain shadow of the remote sensing image to be detected.
Compared with the prior art, in the method for automatically detecting the topographic shadow of the energy-integrated remote sensing image, if the selected threshold has an unsatisfactory effect on segmenting the shadow region, the size of the threshold is continuously adjusted until the satisfactory segmentation effect is obtained, so that the automatic detection of the topographic shadow of the remote sensing image to be detected is completed. The method for automatically detecting the terrain shadow of the remote sensing image based on the energy integration has the advantages of low calculation complexity, high efficiency and convenience; the automatic realization process is simple, and the detection precision is very high in a complicated terrain area.
The method for automatically detecting the topographic shadow of the remote sensing image based on energy integration provided by the embodiment has the following specific application scenes:
one scene of ASTER remote sensing image data (multispectral) in a complex terrain area in south Hunan province is selected at will for experiment, and according to the parameters of the remote sensing image data, the formula (2) is converted into the following formula for calculation:
E(x，y)＝0.5*[(b1+b2)*0.105+(b2+b3)*0.146+(b3+b4)*0.849+(b4+b5)*0.511+(b5+b6)*0.042+(b6+b7)*0.053+(b7+b8)*0.074+(b8+b9)*0.064]
wherein b 1-b 9 represent the single-waveband grayscale images of the ASTER remote sensing image data respectively. 0.105, 0.146, 0.849, etc. respectively represent the band interval or distance of adjacent bands, and are obtained by subtracting the center wavelengths of the bands.
Different remote sensing image data wave band settings are different, therefore, parameters need to be determined according to specific detected data characteristics. In fact, the remote sensing image data has one more spectral dimension than the ordinary image, and the embodiment is mainly realized on the spectral dimension, namely lambda.
The specific detection effect is shown in the following figure 5, and as can be clearly seen from the figure, the method has high detection precision on the multi-spectral remote sensing image terrain shadow in the complex terrain area, and is efficient, convenient and fast and high in automation degree. In fact, this approach has proven to be applicable to many specific scenarios in practice many times. The hyperspectral remote sensing image data has more wave bands, more fine radiation data and better detection effect due to more spectrums generally.
Referring to fig. 6, fig. 6 is a functional block diagram of a first embodiment of the energy-integrated remote sensing image terrain shadow automatic detection system provided by the present invention, in this embodiment, the energy-integrated remote sensing image terrain shadow automatic detection system includes a first obtaining module 10, a second obtaining module 20 and a calculating module 30, wherein the first obtaining module 10 is configured to obtain remote sensing image data, and perform radiometric calibration on the obtained remote sensing image data to obtain a radiant energy spectrum curve; the second obtaining module 20 is configured to perform data clipping on the detection area to be detected, and obtain a remote sensing image band information parameter; and the calculating module 30 is used for calculating integral energy according to the acquired remote sensing image wave band information parameters and the radiation energy spectrum curve.
The first obtaining module 10 obtains the ASTER remote sensing image data, and performs radiometric calibration on the obtained remote sensing image data to obtain a radiant energy spectrum curve r (λ, x, y). Wherein the radiation energy spectral curve r (λ, x, y) represents the radiation energy of the pixel P (x, y) as a function of wavelength.
The second obtaining module 20 cuts data of a remote sensing image area to be detected by the ASTER remote sensing image to obtain a remote sensing image band information parameter. As shown in fig. 4, fig. 4 is an initial image for detecting the terrain shadow of the ASTER image in the method for automatically detecting the terrain shadow of the energy-integrated remote sensing image provided by the present invention, and the information parameters of the bands of the remote sensing image include the spectral dimension, the grayscale map of the single band of the ASTER remote sensing image data, and the band interval or distance between adjacent bands.
The calculation module 30 defines an energy integral function according to the total energy of the radiation received by the shadow area and the non-shadow area, and the specific form is as follows:
Figure BDA0002630829320000091
in equation (3), E (x, y) represents the integrated energy of solar radiation received by pixel P (x, y), λ1Represents the 1 st band; lambda [ alpha ]nRepresents an nth band; r (λ, x, y) represents the radiant energy of the picture element P (x, y) as a function of wavelength.
The remote sensing image data is in a discrete form in a wave band dimension, and an integral equation is converted into an energy integral function expression. Calculating integral energy according to the obtained remote sensing image waveband information parameters and the radiation energy spectrum curve, wherein the integral energy is obtained through the following energy integral function expression:
Figure BDA0002630829320000092
in the formula (4), λnRepresents the nth band spectral dimension; lambda [ alpha ]n+1Represents the n +1 band spectral dimension; λ represents the spectral dimension; r (lambda)n+1X, y) represents the radiation energy of the pixel P (x, y)) as a function of the wavelength of the n +1 th band; r (lambda)nX, y) represents the radiation energy of the picture element P (x, y)) as a function of the wavelength of the nth wavelength band.
Compared with the prior art, the system for automatically detecting the topographic shadow of the remote sensing image based on the energy integration adopts a first acquisition module, a second acquisition module and a calculation module, the first acquisition module is used for acquiring remote sensing image data, and the second acquisition module is used for carrying out radiometric calibration on the acquired remote sensing image data to obtain a radiant energy spectral curve; data cutting is carried out on the detection area to be detected, and the wave band information parameters of the remote sensing image are obtained; and the calculation module calculates integral energy according to the acquired remote sensing image wave band information parameters and the radiation energy spectrum curve. The system for automatically detecting the terrain shadow of the remote sensing image of the energy integral has low calculation complexity, and is efficient and convenient; the automatic realization process is simple, and the detection precision is very high in a complicated terrain area.
Further, referring to fig. 7, fig. 7 is a functional block diagram of a second embodiment of the energy-integrated remote-sensing image terrain shadow automatic detection system according to the present invention, on the basis of the first embodiment, the energy-integrated remote-sensing image terrain shadow automatic detection system further includes a third obtaining module 40, a fourth obtaining module 50, a segmentation module 60, and a detection module 70, wherein the third obtaining module 40 is configured to program the calculated integrated energy by using a graphic processing tool to obtain an energy-integrated gray scale image; a fourth obtaining module 50, configured to count the obtained energy integral gray level image, and obtain a shadow region segmentation threshold; the segmentation module 60 is configured to select an appropriate threshold to segment the shadow region according to the statistical characteristics of the energy integration gray level image; and the detection module 70 is configured to derive a shadow partition contour vector, and then complete automatic detection of the terrain shadow of the remote sensing image to be detected.
The third acquisition module 40 uses Band Math tool of ENVI5.3 to program the calculated integrated energy E (x, y) and acquire an energy integrated gray image.
The statistical characteristic of the fourth obtaining module 50 is generally asymmetric double peak type, the left peak area represents a shadow area image element set, and the right peak area represents a non-shadow area image element set; the left peak is generally lower than the right peak. The trough bottom between the two peaks is the shadow segmentation threshold (multiple experiments).
The segmentation module 60 selects an appropriate threshold to segment the shadow region according to the statistical characteristics of the energy integration gray image.
The segmented contour vector derived by the detection module 70 is shown in fig. 5, and fig. 5 is an image of the detection result of the ASTER image terrain shadow detection in the energy-integrated remote sensing image terrain shadow automatic detection method provided by the invention, and the detection result is a white contour delineation part.
Compared with the prior art, the automatic detection system for the topographic shadow of the remote sensing image of the energy integral provided by the embodiment adopts a third acquisition module, a fourth acquisition module, a segmentation module and a detection module, wherein the third acquisition module utilizes a graphic processing tool to program the calculated integral energy to acquire an energy integral gray image; the fourth acquisition module counts the acquired energy integral gray level image to acquire a shadow region segmentation threshold; the segmentation module selects a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integral gray level image; and the detection module derives the contour vector of the shadow partition area, and then the automatic detection of the terrain shadow of the remote sensing image to be detected is completed. The system for automatically detecting the terrain shadow of the remote sensing image of the energy integral has low calculation complexity, and is efficient and convenient; the automatic realization process is simple, and the detection precision is very high in a complicated terrain area.
Preferably, please refer to fig. 8, where fig. 8 is a functional block diagram of a third embodiment of the energy-integrated remote sensing image terrain shadow automatic detection system provided by the present invention, and on the basis of the second embodiment, the energy-integrated remote sensing image terrain shadow automatic detection system further includes an execution module 80, where the execution module 80 is configured to, if the selected threshold is not ideal for the shadow region segmentation effect, continuously adjust the threshold size until an ideal segmentation effect is obtained, and complete automatic detection of the terrain shadow of the remote sensing image to be detected.
Compared with the prior art, the energy-integrated remote sensing image terrain shadow automatic detection system provided by the embodiment adopts the execution module, and if the selected threshold has an unsatisfactory shadow region segmentation effect, the execution module continues to adjust the threshold until the ideal segmentation effect is obtained, so that the terrain shadow automatic detection of the remote sensing image to be detected is completed. The system for automatically detecting the terrain shadow of the remote sensing image of the energy integral has low calculation complexity, and is efficient and convenient; the automatic realization process is simple, and the detection precision is very high in a complicated terrain area.
The energy-integrated remote sensing image terrain shadow automatic detection system provided by the embodiment has the following specific application scenes:
one scene of ASTER remote sensing image data (multispectral) in a complex terrain area in south Hunan province is selected at will for experiment, and according to the parameters of the remote sensing image data, the formula (4) is converted into the following formula for calculation:
E(x，y)＝0.5*[(b1+b2)*0.105+(b2+b3)*0.146+(b3+b4)*0.849+(b4+b5)*0.511+(b5+b6)*0.042+(b6+b7)*0.053+(b7+b8)*0.074+(b8+b9)*0.064]
wherein b 1-b 9 represent the single-waveband grayscale images of the ASTER remote sensing image data respectively. 0.105, 0.146, 0.849, etc. respectively represent the band interval or distance of adjacent bands, and are obtained by subtracting the center wavelengths of the bands.
Different remote sensing image data wave band settings are different, therefore, parameters need to be determined according to specific detected data characteristics. In fact, the remote sensing image data has one more spectral dimension than the ordinary image, and the embodiment is mainly realized on the spectral dimension, namely lambda.
The specific detection effect is shown in the following figure 5, and as can be clearly seen from the figure, the system has high detection precision on the multi-spectral remote sensing image terrain shadow in the complex terrain area, and is efficient, convenient and fast and high in automation degree. In fact, the system has many times proven in practice to be applicable to many specific scenarios. The hyperspectral remote sensing image data has more wave bands, more fine radiation data and better detection effect due to more spectrums generally.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. An energy-integrated remote sensing image terrain shadow automatic detection method is characterized by comprising the following steps:
acquiring remote sensing image data, and carrying out radiometric calibration on the acquired remote sensing image data to obtain a radiant energy spectrum curve;
data cutting is carried out on the detection area to be detected, and the wave band information parameters of the remote sensing image are obtained;
and calculating integral energy according to the acquired remote sensing image wave band information parameters and the radiation energy spectrum curve.
2. The method of automatically detecting energy-integrated remote-sensing image terrain shadow of claim 1,
the step of calculating the integral energy according to the obtained remote sensing image wave band information parameters and the radiation energy spectrum curve further comprises the following steps:
programming the calculated integral energy by using a graphic processing tool to obtain an energy integral gray image;
counting the acquired energy integral gray level image to acquire a shadow region segmentation threshold;
selecting a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integral gray level image;
and deriving the contour vector of the shadow partition area, and then finishing the automatic detection of the terrain shadow of the remote sensing image to be detected.
3. The method of automatically detecting energy-integrated remote-sensing image terrain shadow of claim 2,
the step of selecting a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integration gray level image further comprises the following steps:
and if the selected threshold has an unsatisfactory shadow region segmentation effect, continuously adjusting the size of the threshold until the satisfactory segmentation effect is obtained, and completing automatic detection of the terrain shadow of the remote sensing image to be detected.
4. The method of automatically detecting energy-integrated remote-sensing image terrain shadow of claim 1,
the integral energy is obtained by the following energy integral function expression:
Figure FDA0002630829310000011
the correspondence and form are as follows:
Figure FDA0002630829310000021
wherein λ isnRepresents the nth band spectral dimension; lambda [ alpha ]n+1Represents the n +1 band spectral dimension; λ represents the spectral dimension; r (lambda)n+1X, y) represents the radiation energy of the pixel P (x, y)) as a function of the wavelength of the n +1 th band; r (lambda)nX, y) represents the radiation energy of the picture element P (x, y)) as a function of the wavelength of the nth wavelength band.
5. The method of automatically detecting energy-integrated remote-sensing image terrain shadow of claim 2,
the statistical characteristic of the energy integration gray scale image is in an asymmetric bimodal mode.
6. The method of automatically detecting energy-integrated remote-sensing image terrain shadow of claim 5, characterized in that,
the energy integration gray image comprises a left peak area and a right peak area, wherein the left peak area represents a shadow area image element set, and the right peak area represents a non-shadow area image element set; and the valley bottom between the left peak area and the right peak area is a shadow area segmentation threshold.
7. An energy-integrated remote sensing image terrain shadow automatic detection system is characterized by comprising:
the first acquisition module (10) is used for acquiring remote sensing image data and carrying out radiometric calibration on the acquired remote sensing image data to obtain a radiant energy spectrum curve;
the second acquisition module (20) is used for cutting data of the remote sensing image area to be detected and acquiring the wave band information parameters of the remote sensing image;
and the calculating module (30) is used for calculating integral energy according to the acquired remote sensing image wave band information parameters and the radiation energy spectrum curve.
8. The energy-integrated remote-sensing image terrain shadow automatic detection system of claim 7,
the remote sensing image terrain shadow automatic detection system of energy integration still includes:
a third acquisition module (40) for programming the calculated integral energy by using a graphic processing tool to acquire an energy integral gray image;
the fourth acquisition module (50) is used for counting the acquired energy integral gray level images and acquiring a shadow region segmentation threshold;
the segmentation module (60) is used for selecting a proper threshold value to segment the shadow region according to the statistical characteristics of the energy integral gray level image;
and the detection module (70) is used for deriving the contour vector of the shadow partition area, and then finishing automatic detection of the terrain shadow of the remote sensing image to be detected.
9. The energy-integrated remote-sensing image terrain shadow automatic detection system of claim 7,
the remote sensing image terrain shadow automatic detection system of energy integration still includes:
and the execution module (80) is used for continuously adjusting the threshold value until an ideal segmentation effect is obtained if the segmentation effect is not ideal, deriving the contour vector of the shadow segmentation area, and completing automatic detection of the terrain shadow of the remote sensing image to be detected.
10. The energy-integrated remote-sensing image terrain shadow automatic detection system of claim 7,
the integral energy is obtained by the following energy integral function expression:
Figure FDA0002630829310000031
the correspondence and form are as follows:
Figure FDA0002630829310000032
wherein λ isnRepresents the nth band spectral dimension; lambda [ alpha ]n+1Represents the n +1 band spectral dimension; λ represents the spectral dimension; r (lambda)n+1X, y) represents the radiation energy of the pixel P (x, y)) as a function of the wavelength of the n +1 th band; r (lambda)nX, y) represents the radiation energy of the picture element P (x, y)) as a function of the wavelength of the nth wavelength band.
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