A method and system for quickly extracting an erosion gully morphology index

Abstract

The application discloses a kind of erosion ditch morphological index quick extraction method and system, belong to water and soil conservation technical field.The method includes: S1, basin segmentation;S2, erosion ditch object identification;Including: based on orthographic image, spectral feature analysis is carried out, and first principal component component is obtained;Texture feature variable and topographic feature variable are selected;First principal component component is carried out multiscale image segmentation based on texture feature variable;According to topographic feature variable, whether each object is erosion ditch is identified;The identified result is corrected, and the final erosion ditch object identification result is obtained;S3, erosion ditch is carried out channel line extraction;Through hydrological analysis, river network is extracted and then corrected, and the corrected river network is used as the channel line of erosion ditch;S4, according to erosion ditch object identification result and channel line, the morphological index of erosion ditch is extracted.The application can effectively improve the precision of erosion ditch morphological feature extraction, and break through the problem of poor erosion ditch extraction precision.

Description

Technical Field

[0001] The present invention belongs to the technical field of soil and water conservation, and in particular relates to a method and system for quickly extracting erosion gully morphological indicators. Background Art

[0002] The water conservancy department has achieved full coverage of annual dynamic monitoring of soil and water loss, using a combination of remote sensing monitoring, field surveys, model calculations, and statistical analysis to carry out factor extraction, modulus calculations, and dynamic analysis and evaluation of soil and water loss. Since the monitoring scope covers the entire country, higher requirements are placed on image resolution and temporal phase, and it is necessary to explore the application of images with higher temporal and spatial resolutions to carry out work. Among them, topography is one of the important factors affecting soil and water loss. Both slope and slope length factors need to be extracted based on large-scale topographic maps or high-precision DEMs. The Gaofen-7 satellite (GF-7) is an outstanding representative of the series of satellites of China's High-Resolution Earth Observation System Major Project (GF Project). It can obtain full-color stereo images with a resolution better than 0.8m, meeting the urgent need for high-precision stereo mapping data based on remote sensing surveys and model calculations. It is very necessary to improve the quantification accuracy of soil and water loss. The high-precision stereo images it obtains provide a basis for improving the accuracy of terrain factor calculations.

[0003] In recent years, the country has implemented gully control projects, achieving significant results in soil and water loss control. Research on gully and channel changes, providing a scientific basis for precise gully and channel control, is a development direction and urgent need for high-resolution remote sensing applications in soil and water conservation. At the spatial scale, domestic satellite remote sensing imagery has traditionally had low mapping accuracy, limited to a 1:50,000 scale, which cannot meet the needs of small-scale soil and water conservation monitoring. Topographic data such as slope, length, and shape have primarily been acquired using digital elevation models (DEMs) generated from 1:50,000 data maps, which have significant accuracy limitations. At the temporal scale, real-time remote sensing data acquisition has traditionally been difficult and time-consuming, resulting in insufficient baseline data for monitoring and research purposes. This has severely hampered the development and application of high-resolution remote sensing-based soil and water conservation monitoring and evaluation, as well as research on gully and channel changes. The soil and water conservation monitoring and evaluation work based on high-resolution remote sensing is still at a relatively low level. There is an urgent need for domestically produced GF7 high-resolution remote sensing image data with full coverage, high precision, fast update, and the ability to achieve stereo mapping. We also need to explore methods for extracting gully indicators from domestically produced GF7 stereo mapping image data to promote and improve the level of soil and water conservation monitoring and evaluation work. Summary of the Invention

[0004] The present invention aims to solve one of the technical problems in the above-mentioned related art at least to a certain extent.

[0005] To this end, the purpose of the present invention is to provide a method and system for quickly extracting erosion gully morphological indicators, which can effectively improve the accuracy of erosion gully morphological feature extraction and overcome the problem of poor erosion gully extraction accuracy.

[0006] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0007] An embodiment of the present invention provides a method for quickly extracting erosion gully morphological indicators, the method comprising:

[0008] S1. Segment the watershed based on DEM data to obtain several watershed units;

[0009] S2. Identify gully objects in the watershed unit. The identification content includes:

[0010] Based on the orthophoto of the watershed unit, spectral feature analysis is performed to extract the red, green, and blue band images and construct erosion gully interpretation signs;

[0011] Perform principal component transformation on the extracted image and select the component with the most three-band information as the first principal component;

[0012] Selecting a number of texture feature variables and a number of terrain feature variables required for subsequent processing;

[0013] Based on the selected texture feature variables, multi-scale image segmentation is performed on the first principal component;

[0014] Identify whether each object in the watershed unit is an erosion gully based on the segmented terrain characteristic variables;

[0015] Correct the recognition results to obtain the final erosion gully object recognition results;

[0016] S3, extracting the channel line of the erosion gully;

[0017] Based on the DEM data of the current watershed unit, a hydrological analysis is performed to extract the river network and then correct it. The final river network is used as the channel line of the erosion gully.

[0018] S4, extracting morphological indicators of erosion gullies;

[0019] Based on the erosion gully object recognition results of S2 and the gully line of S3, the morphological indicators of the erosion gully are extracted.

[0020] In addition, the method for quickly extracting erosion gully morphological indicators according to the present invention may also have the following additional technical features:

[0021] In some embodiments, the first principal component accounts for more than 99% of the information of the red, green and blue bands.

[0022] In some embodiments, the selected texture feature variables include contrast, dissimilarity, homogeneity, angular second moment, entropy, mean, variance, and correlation.

[0023] In some embodiments, the selected terrain characteristic variables include elevation, slope, sunlight simulation, and runoff accumulation.

[0024] In some embodiments, the rule for identifying whether each object in a watershed unit is an erosion gully is:

[0025] Objects with a negative terrain ratio greater than a first threshold are identified as erosion gullies, and objects with a slope less than a second threshold are identified as non-erosion gullies, or

[0026] Objects in the shaded area are identified as erosion gullies.

[0027] In some implementations, performing hydrological analysis on DEM data to extract river network content includes:

[0028] Extract river network based on D8 algorithm and Bumin algorithm;

[0029] Based on the extraction results, the Strahler classification method is used to classify the extracted river network system, and the buffering analysis of rivers at all levels is performed. The difference between the average elevation within the river line and the average elevation within its buffer zone is calculated respectively. The channels with a difference greater than the third threshold are identified as erosion channels, and the final river network system is obtained by combining the extracted river network system.

[0030] In some of the embodiments, the water catchment threshold is set to 800 when extracting river network water systems based on the D8 algorithm and the Burn in algorithm.

[0031] In some embodiments, gully morphological indicators are extracted from three scales: watershed, gully, and channel.

[0032] In some embodiments, the morphological indicators at the watershed scale include: gully area, gully length, gully density, and gully fragmentation;

[0033] The morphological indicators at the gully scale include: the main gully to branch gully ratio and the longitudinal gradient of the main gully; the morphological indicators at the gully scale include:

[0034] The morphological indicators of channel scale include: channel area, channel length and channel depth.

[0035] An embodiment of the present invention further provides a system for rapidly extracting erosion gully morphological indicators, which is characterized in that it can implement any of the above methods for rapidly extracting erosion gully morphological indicators; the system comprises:

[0036] The watershed segmentation module is used to segment the watershed based on DEM data and obtain several watershed units;

[0037] The gully object recognition module is used to identify gully objects in watershed units. The recognition process includes: performing spectral feature analysis based on the orthophoto of the watershed unit, extracting red, green, and blue band images, and constructing gully interpretation symbols; performing principal component transformation on the extracted image, selecting the component with the most information from the three bands as the first principal component; selecting several texture feature variables and several terrain feature variables required for subsequent processing; performing multi-scale image segmentation on the first principal component based on the selected texture feature variables; identifying whether each object in the watershed unit is an gully based on the segmented terrain feature variables; and correcting the recognition results to obtain the final gully object recognition results.

[0038] The channel line extraction module is used to extract the channel line of the erosion gully. The specific contents include: performing hydrological analysis based on the DEM data of the current watershed unit, extracting the river network and then correcting it, and using the final river network as the channel line of the erosion gully;

[0039] The morphological index extraction module is used to extract the morphological index of the erosion gully based on the erosion gully object recognition result and the gully line extraction result.

[0040] Compared with the prior art, the present invention has at least the following beneficial effects:

[0041] In an embodiment of the present invention, a rapid method for extracting gully morphological indicators uses principal component analysis to reduce data redundancy in Gaofen-7 imagery and improve algorithm efficiency. Orthophoto segmentation is performed using the first principal component after dimensionality reduction processing using principal component analysis. A classification algorithm and rule set are constructed, combining the topographic characteristics of gullies with Gaofen-7 stereo mapping data. The positive and negative terrain method, slope threshold method, and spectral feature analysis are used to extract gully locations. Compared to traditional erosion extraction methods, this method has the advantages of high extraction accuracy, fast speed, and strong robustness.

[0042] In an embodiment of the present invention, a rapid method for extracting gully morphological indicators uses hydrological analysis to extract river networks and gully lines from the DEM generated by Gaofen-7 stereoscopic mapping remote sensing data. The D8 and Burn-in algorithms are used for correction, and buffer zone analysis is performed on primary, secondary, and tertiary river channels. Thresholds are set to eliminate pseudo-gullies. This method significantly improves extraction accuracy compared to traditional gully line extraction methods.

[0043] In an embodiment of the present invention, a method for rapidly extracting gully morphological indicators is provided. Based on the extraction structure of gully and gully channel lines, gully morphological indicators such as gully area, gully length, gully density, gully fragmentation, longitudinal gradient of the main channel, gully area, gully length, and gully depth are extracted from three scales: watershed, gully, and channel. This method can be applied in a business and engineering manner in the field of soil and water conservation.

[0044] The system for rapidly extracting erosion gully morphology indicators of the present invention is capable of implementing the aforementioned method for rapidly extracting erosion gully morphology indicators, and thus possesses at least all of the features and advantages of the aforementioned method for rapidly extracting erosion gully morphology indicators, which are not further elaborated herein. Additional aspects and advantages of the present invention will be described in part in the following description and will become apparent from the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of a method for rapidly extracting erosion gully morphological indicators disclosed in one embodiment of the present invention;

[0046] Figure 2 This is a flowchart of object-oriented erosion gully extraction disclosed in one embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] The embodiments of the present invention are described in detail below through specific embodiments and application scenarios with reference to the accompanying drawings.

[0049] See also Figure 1 As shown, in some embodiments of the present invention, a method for quickly extracting erosion gully morphological indicators is provided, and the steps of the method include:

[0050] Step 1: Watershed division

[0051] A watershed is a natural geographical boundary with strict geographical significance and has multiple levels of characteristics. In addition, erosion gullies often show different characteristics in different watersheds, such as quantity, spatial shape, and distribution pattern. Therefore, we first segment the watershed based on the DEM data and set the 2.5km 2 The watershed unit is divided into approximately 2.5 km2 according to the cumulative watershed threshold. 2 Small watershed unit.

[0052] Step 2: Characteristic analysis of erosion gully objects

[0053] 1) Spectral characteristics

[0054] Based on the high-resolution orthophoto imagery obtained after preprocessing with the Gaofen-7 satellite, we extracted the red (R), green (G), and blue (B) bands. We analyzed the image features and constructed gully interpretation landmarks. These landmarks served as the basis for subsequent steps and served as a reference for the ground truth.

[0055] 2) Principal Component Analysis

[0056] Principal component transformation (PCA) is first performed on the remote sensing image, followed by principal component analysis (PCA). The principal component factors are then extracted to enhance the image's spectral information. A detailed table of components from the PCA analysis (Table 1) shows that the first principal component encompasses 99.2% of the information from the red, green, and blue bands. Selecting the first principal component for subsequent gully extraction, while ignoring the second and third principal components, effectively reduces data redundancy and improves algorithm efficiency.

[0057] Table 1 Image texture feature calculation formula and meaning

[0058]

[0059] 3) Texture features

[0060] Texture features are also commonly used in the research of object classification and extraction. They contain relatively rich image information and are a global feature that reflects the slowly changing or periodic surface tissue structure arrangement properties of the surface of the object. Eight texture features of the first principal component based on principal component analysis are selected for subsequent analysis and extraction. The eight texture features are contrast, difference, homogeneity, angular second moment, entropy, mean, variance and correlation.

[0061] 4) Topographic features

[0062] Erosion gullies have obvious terrain characteristics. Therefore, when performing image segmentation for erosion gully extraction, terrain characteristics will contain more useful information. Four terrain characteristic variables were selected for subsequent analysis and extraction: elevation, slope, illumination simulation, and runoff accumulation.

[0063] 5) Object-oriented erosion gully extraction method

[0064] First, based on the first principal component extracted by principal component analysis and dimensionality reduction, multi-scale image segmentation is performed on the first principal component. Then, the classification algorithm set and rule set are constructed in combination with the terrain characteristics of the erosion gully object, such as Figure 2As shown, objects with a negative terrain ratio greater than a certain threshold are identified as gullies, while objects with a slope less than a certain threshold are identified as non-gully objects. Finally, shadow areas are identified as gully objects based on the spectral characteristics of gully objects. In other words, an gully object is identified as such if any of the following three conditions is met: a negative terrain ratio greater than a certain threshold, a slope not less than a certain threshold, or the object is considered a shadow area. The threshold for negative terrain depends on the specific study area. The specific method for identifying shadow areas can be based on existing techniques and will not be elaborated on in this invention.

[0065] Secondly, objects extracted by GF-7 based on the negative terrain method may be missed or incorrectly extracted, requiring manual interpretation and correction. The following explains the basis for manual correction. In order to intuitively express the color tone, vividness, and brightness of different land features and increase contrast, the GF-7 RGB orthophoto is converted to HSV imagery, and interpretation is performed based on the HSV imagery. For areas such as roads and houses in flat areas connected to erosion gullies, non-erosion areas may be mistakenly identified as erosion gullies. This can be corrected by using the slope threshold method, where areas with slopes less than 15° are excluded. For (I) areas where HSV images appear orange on shady slopes and missed areas, (II) areas where HSV images appear blue on sunny slopes and missed areas between gullies, and (III) areas where RGB orthophoto images appear gray and HSV images appear green, where non-erosion gullies are mistakenly identified as erosion gullies, an index VI = HV is constructed, and areas with VI < 0 are extracted for missed and incorrect extraction corrections. For areas where the shadow areas between sunny slopes and gullies appear blue in HSV images, areas where the shadow areas appear gray in RGB orthophoto images, areas where non-erosion gullies appear green in HSV images, and areas where non-erosion gullies are mistakenly identified as erosion gullies, and spectral characteristics cannot be well classified with other land features, human-computer interaction is used to correct them.

[0066] Step 3: Extraction of erosion gully lines

[0067] River networks extracted through hydrological analysis based on DEM data closely match the gully lines of erosion gullies. Therefore, DEM-derived river networks can be used to extract erosion gullies. However, river networks generated directly from DEMs differ from actual gully lines. Different confluence thresholds must be set for different gully types. If the threshold is set too high, the extracted river systems often fail to reach the gully head, resulting in shorter gullies and missing erosion gullies. Setting a lower threshold can lead to parallel and spurious gullies. By combining the D8 and Bumin algorithms to correct the original DEM, these parallel and spurious gullies can be reduced.

[0068] The river network is extracted based on the D8 and Burn in algorithms, and the river network is classified using the Strahler classification method. In this embodiment, the water collection threshold is set to 800, which can ensure that the extracted river channel can reach the head of the erosion gully, but there will be some pseudo-rivers. For the first-level, second-level, and third-level river channels, a suitable radius is set to perform a buffer analysis, and the difference between the average elevation in the river channel line and the average elevation in its buffer zone is calculated respectively. The gullies with a difference greater than the set threshold are identified as erosion gullies. The buffer analysis can be implemented using GIS software. Although some pseudo-gullies can be eliminated based on this method, there are still some that cannot be identified and need to be manually corrected.

[0069] Step 4: Morphological indicator extraction

[0070] The erosion gully morphological index is mainly a statistical index based on the extraction of erosion gullies and channel lines. The erosion gully morphological index is extracted from three scales: watershed, gully and channel. The specific extraction indexes are shown in Table 2.

[0071] Table 2 Calculation formula and meaning of erosion gully morphological index

[0072]

[0073]

[0074] 1) Basin scale

[0075] Taking the small watershed as the analysis unit, the area and length of the erosion gully, the density of the erosion gully, and the degree of erosion gully fragmentation are statistically analyzed. The area of ​​the erosion gully is directly based on the erosion gully spatial distribution data product and statistically analyzed with the watershed as the unit. The length of the erosion gully is calculated by combining the watershed network with the range of the erosion gully. First, taking the small watershed as the unit, based on the DEM data, a smaller watershed threshold is set to ensure that the watershed grid contains the actual boundary of the erosion gully. The watershed network is corrected using the boundary of the erosion gully to ensure the adaptability of the gully head area to the watershed network. Finally, the length of the erosion gully is statistically calculated using the small watershed as the unit. Based on the length and area of ​​the erosion gully, the density and degree of fragmentation of the erosion gully are calculated. The density of the erosion gully is the ratio of the erosion gully length to the watershed area, and the degree of fragmentation of the erosion gully is the ratio of the erosion gully area to the watershed area.

[0076] 2) Ravine scale

[0077] Taking the erosion gully range as the analysis unit, the main gully to branch gully ratio and the main gully longitudinal gradient are calculated. The main gully to branch gully ratio is the ratio of the main gully length to the total gully length.

[0078] Based on the DEM, the drainage network is extracted. The main channel source is determined based on the direction of water flow. The main channel and tributaries are identified. Based on the grid vectors of the drainage grid, the main channel to tributary ratio is calculated. This can be accomplished using ArcGIS software. The longitudinal gradient of the main gully is the ratio of the elevation drop to the length of the main gully. Based on the main gully extracted from the drainage grid, the main gully head and tail are identified, and the elevation difference is calculated to calculate the longitudinal gradient of the gully.

[0079] 3) Channel scale

[0080] Based on the extraction of erosion gullies, the erosion gullies are taken as the research objects, the area of ​​the gullies is extracted, and regional analysis is carried out in combination with DEM. Arcgis software can be used to count the corresponding highest and lowest elevations in the erosion gully unit, and calculate the difference to extract the gully depth.

[0081] Parts of the present invention that are not described in detail are well known to those skilled in the art.

[0082] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A method for quickly extracting erosion gully morphological indicators, characterized in that: The method comprises: S1. Segment the watershed based on DEM data to obtain several watershed units; S2. Identify gully objects in the watershed unit. The identification content includes: Based on the orthophoto of the watershed unit, spectral feature analysis is performed to extract the red, green, and blue band images and construct erosion gully interpretation signs; Perform principal component transformation on the extracted image and select the component with the most three-band information as the first principal component; Selecting a number of texture feature variables and a number of terrain feature variables required for subsequent processing; Based on the selected texture feature variables, multi-scale image segmentation is performed on the first principal component; Identify whether each object in the watershed unit is an erosion gully based on the segmented terrain characteristic variables; Correct the recognition results to obtain the final erosion gully object recognition results; S3, extracting the channel line of the erosion gully; Based on the DEM data of the current watershed unit, a hydrological analysis is performed to extract the river network and then correct it. The final river network is used as the channel line of the erosion gully. S4, extracting morphological indicators of erosion gullies; According to the erosion gully object recognition results of S2 and the gully line of S3, the morphological indicators of the erosion gully are extracted; The contents of river network extracted from DEM data through hydrological analysis include: Extract river network based on D8 algorithm and Burn in algorithm; Based on the extraction results, the Strahler classification method is used to classify the extracted river network system, and the buffering analysis of rivers at all levels is performed. The difference between the average elevation within the river line and the average elevation within its buffer zone is calculated respectively. The channels with a difference greater than the third threshold are identified as erosion channels, and the final river network system is obtained by combining the extracted river network system.

2. The method for rapidly extracting erosion gully morphological indicators according to claim 1, characterized in that: The first principal component accounts for more than 99% of the information of the red, green and blue bands.

3. The method for rapidly extracting erosion gully morphological indicators according to claim 1, characterized in that: The selected texture feature variables include contrast, dissimilarity, homogeneity, angular second moment, entropy, mean, variance and correlation.

4. The method for rapidly extracting erosion gully morphological indicators according to claim 1, characterized in that: The selected terrain characteristic variables include elevation, slope, sunlight simulation and runoff accumulation.

5. The method for rapidly extracting erosion gully morphological indicators according to claim 1, characterized in that: The rules for identifying whether each object in a watershed unit is an erosion gully are: Objects with a negative terrain ratio greater than a first threshold are identified as erosion gullies, and objects with a slope less than a second threshold are identified as non-erosion gullies, or Objects in the shaded area are identified as erosion gullies.

6. The method for rapidly extracting erosion gully morphological indicators according to claim 1, characterized in that: The water catchment threshold for river network extraction based on the D8 algorithm and Burn in algorithm is set to 800.

7. The method for rapidly extracting erosion gully morphological indicators according to claim 1, characterized in that: Erosion gully morphological indicators are extracted from three scales: watershed, gully and channel.

8. The method for rapidly extracting gully morphological indicators according to claim 7, characterized in that: The morphological indicators at the watershed scale include: gully area, gully length, gully density and gully fragmentation; The morphological indicators at the gully scale include: the main gully to branch gully ratio and the longitudinal gradient of the main gully; the morphological indicators at the gully scale include: The morphological indicators of channel scale include: channel area, channel length and channel depth.

9. A rapid extraction system for erosion gully morphological indicators, characterized in that: A method for rapidly extracting erosion gully morphological indicators according to any one of claims 1 to 8 can be implemented; the system comprises: The watershed segmentation module is used to segment the watershed based on DEM data and obtain several watershed units; The gully object recognition module is used to identify gully objects in watershed units. The recognition process includes: performing spectral feature analysis based on the orthophoto of the watershed unit, extracting red, green, and blue band images, and constructing gully interpretation symbols; performing principal component transformation on the extracted image, selecting the component with the most information from the three bands as the first principal component; selecting several texture feature variables and several terrain feature variables required for subsequent processing; performing multi-scale image segmentation on the first principal component based on the selected texture feature variables; identifying whether each object in the watershed unit is an gully based on the segmented terrain feature variables; and correcting the recognition results to obtain the final gully object recognition results. The channel line extraction module is used to extract the channel line of the erosion gully. The specific contents include: performing hydrological analysis based on the DEM data of the current watershed unit, extracting the river network and then correcting it, and using the final river network as the channel line of the erosion gully; The morphological index extraction module is used to extract the morphological index of the erosion gully based on the erosion gully object recognition result and the gully line extraction result.

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