CN104361338B - A kind of peat bog information extracting method based on ENVISAT ASAR, Landsat TM and dem data - Google Patents

Abstract

A peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data, the invention relates to a peat swamp information extraction method. The invention solves the problem that traditional methods are difficult to distinguish peat bogs from other types of swamps. The method uses 1 to preprocess Landsat TM data; 2 to preprocess ENVISAT ASAR data; 3 to resample ENVISAT ASAR data; 4 to obtain ENVISAT ASAR images; 5 to obtain Slope data; 6. Extract the backscatter coefficient; 7. Determine the best polarization mode band of ENVISAT ASAR image; 8. Obtain the segmentation unit; 9. Extract characteristic parameters; 10. Determine the best classification band; Vector file; 13 steps to make a peat bog map. The invention is applied to the field of peat swamp information extraction.

Description

A Peat Swamp Information Based on ENVISAT ASAR, Landsat TM and DEM Data information extraction method

technical field

The invention relates to an information extraction method, in particular to a peat swamp information extraction method.

Background technique

Peat swamp is one of the main types of wetlands, which plays an important role in maintaining regional ecological balance and sustainable development. In addition, due to the huge carbon storage in peat swamps, accounting for about 1/3 of the global terrestrial carbon pool, equivalent to 75% of the carbon content in the atmosphere, peat swamps play a pivotal role in global climate change and ecosystem balance status. In recent years, experts and scholars at home and abroad have used different remote sensing image data to study the spatial information extraction of wetland types such as herbaceous wetlands, forest wetlands, and coastal mangrove wetlands. Relatively small.

Optical remote sensing data has the advantages and characteristics of rich spectral information, high cost performance, easy acquisition and simple data processing. However, due to the influence of vegetation on the ground surface, wetlands and non-wetlands can be distinguished using traditional medium and low resolution optical remote sensing images, but it is difficult to distinguish different types of swamps, and it is even more difficult to distinguish different types of peat swamps. Radar remote sensing data has a longer wavelength than optical remote sensing data, and its characteristics of penetrating clouds and fog make it free from time and meteorological constraints when monitoring areas where swamps are widely developed. Moreover, the backscattering of radar images is more sensitive to the dielectric properties (soil moisture, vegetation water content) and geometric characteristics (surface roughness) of the imaging surface. item information. Low-frequency radar bands (P-band and L-band) are more suitable for monitoring wetlands dominated by woodlands, while high-frequency radar bands (C-band) are suitable for studying herbaceous swamps and peat swamps.

The object-oriented interpretation method not only takes into account the spectral information of the ground objects, but also takes into account the geometric and structural features of the ground objects. The smallest unit of image interpretation is to have the same characteristics (such as spectrum, texture and Features such as spatial combination relationship) Homogeneous and uniform objects. Compared with the traditional remote sensing interpretation method that interprets the characteristics of a single pixel of the image, this method breaks through the limitations of the traditional remote sensing classification method that uses pixels as the basic classification and processing unit to contain more semantic information The object composed of multiple adjacent pixels of the remote sensing image is a processing unit, which can realize higher-level remote sensing image classification and target feature extraction. This method is a remote sensing information extraction method based on a cognitive model, which is closer to the human cognitive process, and has become one of the main research directions in the field of remote sensing information extraction. The object-oriented interpretation method emerges at the historic moment in response to the shortcomings of the previous pixel-oriented interpretation methods. The shortcomings of the object-oriented interpretation method are not mentioned in the current research.

Landsat is a series of land resource satellites launched by NASA since 1972. The sensor TM carried by Landsat5 contains 7 wavebands (0.45～0.53μm, 0.52～0.60μm, 0.63～0.69μm, 0.76～0.90μm, 1.55～1.75μm, 10.40～12.50μm, 2.08～2.35μm), orbital height 705km , the spatial resolution is 30m, and the revisit cycle is 16 days. ENVISAT is a giant environmental monitoring satellite launched by the European Space Agency in March 2002. ASAR (Advanced Synthetic Aperture Radar) is an advanced synthetic aperture radar carried by ENVISAT. It has multi-mode, multi-polarization, large width, and multiple incident angles. and other characteristics. Digital elevation model (Digital Elevation Model, referred to as DEM), which is a solid ground model that represents the ground elevation in the form of a set of ordered numerical arrays.

The high-frequency radar band (C-band) is suitable for the study of herbaceous swamps and peat swamps. It is the conclusion that the existing research has drawn. Based on this conclusion, this study selected the C-band radar images when extracting peat swamps, that is, ENVI SAT used in the study. Because the research only involves radar images in the C-band, and does not involve radar images in other bands, it does not involve solving the problem that the high-frequency radar band (C-band) is suitable for studying herbaceous swamps and peat swamps.

Contents of the invention

The purpose of the present invention is to solve the problem that it is difficult to distinguish peat swamps from other swamp types using traditional medium and low resolution optical remote sensing images, and propose a peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data .

Above-mentioned purpose of the invention is achieved through the following technical solutions:

Step 1: Preprocessing the Landsat TM data;

Step 2: Preprocessing the ENVISAT ASAR data;

Step 3: Resample the ENVISAT ASAR data preprocessed in Step 2. The resampled ENVISAT ASAR data has the same grid size as the Landsat TM data processed in Step 1;

Step 4: Use the function of adding control points provided by the Georeferencing module of ArcGIS software to select control points on the preprocessed Landsat TM data, and register the resampled ENVISAT ASAR data according to the control point space to obtain the ENVISAT ASAR image;

Step 5: Extract the slope from the DEM data to obtain slope data;

Step 6: Based on the land cover type survey sample points, extract the backscatter coefficients of radar images under different polarization modes for different land cover types from the ENVISAT ASAR image completed in step 4;

Step 7: Analyze the difference in radar backscatter coefficients between peat swamps and other different land cover types under different polarization modes, and determine the optimal polarization mode band for ENVISAT ASAR images, that is, the optimal polarization mode for radar images extracted from peat swamps band;

Step 8: Carry out multi-layer and multi-scale segmentation on the preprocessed Landsat TM data, slope data and the optimal polarization mode band of the ENVISATASAR image determined in step 7 to obtain a series of segmentation units;

Step 9: Extract feature parameters from a series of segmentation units completed in step 8; wherein, the feature parameters include the average value of each band, normalized vegetation index, normalized water index, TM2+TM3-TM4-TM5 and hue ;

Step ten: according to the feature parameter extracted in step nine, utilize the JM distance method to determine the best classification band;

Step 11: According to the optimal classification band determined in step 10, a classification decision tree is established with reference to the survey sample points of land cover types; among them, the survey sample points of reference land cover types include peat swamp, herbaceous swamp, residential area, traffic land, farmland , forest land, water body land cover type;

Step 12: Run the classification decision tree, export the land cover type classification results, and produce the land cover type vector file; where the land cover type vector file includes farmland, forest land, water body, residential traffic land, herbaceous swamp and peat swamp land cover types ;

Step 13: Make a peat swamp thematic map based on the land cover type vector file completed in step 12; that is, a peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data is completed.

Invention effect

Distinguish other swamp types that are easily confused with peat information, and automatically and quickly and accurately extract the spatial distribution information of peat swamps in medium-resolution remote sensing images (Landsat TM ), so as to realize the method of automatic extraction of peat swamp information for thematic mapping.

Combining radar images with optical images, taking terrain factors as the control factors of peat swamps, comprehensively using object-oriented and decision tree remote sensing classification methods to obtain the characteristic parameters of objects, and selecting the best classification band by JM distance method to establish a decision tree Complete the classification and make a peat swamp thematic map.

Based on Landsat TM data, ENVISAT ASAR images and DEM data, the present invention applies object-oriented and decision tree classification methods to the automatic extraction of peat swamp information, and merges independent pixels into homogeneous objects. In the process of object segmentation, not only Considering spectral features, texture features and topological features, and then by selecting the optimal band and establishing a decision tree, the spatial distribution information of peat swamps is gradually obtained. The accuracy of the obtained classification results is 93%, which is 5%-8% higher than that of the existing method of extracting peat bogs using medium-resolution remote sensing images. At the same time, considering the influence of topography on the distribution of peat bogs, the classification results have clear geographical significance. The invention overcomes the phenomenon of omission and misclassification that occurred when only optical images were used in the extraction of peat swamp information in the past, and also solves the "salt and pepper phenomenon" and "enclave phenomenon" in the peat swamp spatial information obtained by classification, and does not have a clear geographical location. questions of meaning. The invention has practical significance for the combination of optical image, radar image and DEM to quickly and automatically extract peat swamp information.

Description of drawings

Fig. 1 is a flow chart of a peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data proposed in Embodiment 1;

Fig. 2 is a thematic map of peat swamps made from land cover type vector files proposed in Embodiment 1.

detailed description

Embodiment 1: A peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data in this embodiment is specifically prepared according to the following steps:

Step 1: Preprocessing the Landsat TM data;

Step 2: Preprocessing the ENVISAT ASAR data;

Step 3: Resample the ENVISAT ASAR data preprocessed in Step 2 in ArcGIS. The resampled ENVISAT ASAR data has the same grid size as the Landsat TM data processed in Step 1;

Step 4: Based on the preprocessed Landsat TM data, compare the preprocessed Landsat TM data and the resampled ENVISAT ASAR data in the ArcGIS software, and use the function of adding control points provided by the Georeferencing module of the ArcGIS software Select the control points on the preprocessed Landsat TM data, and register the resampled ENVISAT ASAR data according to the control point space to obtain the ENVISAT ASAR image;

Step 5: Use the Aspect command in Surface Analysis under the Spatial Analyst module in ArcGIS software to extract the slope from the DEM data to obtain slope data;

Step 6: Based on the land cover type survey sample points, extract the backscatter coefficients of radar images in ArcGIS for different land cover types and different polarization modes from the ENVISAT ASAR image completed in step 4;

Step 7: Analyze the difference in radar backscatter coefficients between peat swamps and other different land cover types under different polarization modes, and determine the optimal polarization mode band for ENVISAT ASAR images, that is, the optimal polarization mode for radar images extracted from peat swamps band;

Step 8: Use eCognition software to perform multi-layer and multi-scale segmentation on the preprocessed Landsat TM data, slope data, and the optimal polarization mode band of the ENVISAT ASAR image determined in step 7, to obtain a series of segmentation units, and use each segmentation unit as an object;

Step 9: Use eCognition software to extract the characteristic parameters of a series of segmentation units completed in step 8; wherein, the characteristic parameters include the average value of each band, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), TM2+TM3-TM4-TM5, Hue(R:G:B=TM5:TM4:TM3) and Hue(R:G:B=TM4:TM3:TM2);

Step ten: according to the feature parameters extracted in step nine, use the JM distance (Jeffreys MatusitaDistance) method to determine the best classification band;

Step 11: According to the best classification band determined in step 10, refer to the survey sample points of land cover type, and use See5.0 software to establish a classification decision tree; among them, the reference land cover type survey sample points include peat swamp, herbaceous swamp, residential area , transportation land, farmland, forest land, water body and other land cover types;

Step 12: Run the classification decision tree in eCognition software, export the classification results of land cover types, and produce land cover type vector files; among them, the land cover type vector files include farmland, forest land, water body, residential traffic land, herbaceous swamp and peat swamp other land cover types;

Step 13: In the Layout View mode of the ArcGIS software, make a peat swamp thematic map based on the land cover type vector file completed in step 12 (the schematic diagram of the peat swamp thematic map is shown in Figure 2); that is, a ENVISAT-based The peat swamp information extraction method of ASAR, Landsat TM and DEM data is shown in Figure 1.

The effect of this implementation mode:

Distinguish other swamp types that are easily confused with peat information, and automatically and quickly and accurately extract the spatial distribution information of peat swamps in medium-resolution remote sensing images (Landsat TM ), so as to realize the method of automatic extraction of peat swamp information for thematic mapping.

Combining radar images with optical images, taking terrain factors as the control factors of peat swamps, comprehensively using object-oriented and decision tree remote sensing classification methods to obtain the characteristic parameters of objects, and selecting the best classification band by JM distance method to establish a decision tree Complete the classification and make a peat swamp thematic map.

This embodiment is based on Landsat TM data, ENVISAT ASAR images and DEM data, and applies object-oriented and decision tree classification methods to the automatic extraction of peat swamp information, and merges independent pixels into homogeneous objects. Not only spectral features, but also texture features and topological features are considered, and then the spatial distribution information of peat swamps is gradually obtained by selecting the optimal band and establishing a decision tree. The accuracy of the obtained classification results is 93%, which is 5%-8% higher than that of the existing method of extracting peat bogs using medium-resolution remote sensing images. At the same time, considering the influence of topography on the distribution of peat bogs, the classification results have clear geographical significance. This embodiment overcomes the phenomenon of omission and misclassification that occurred when only optical images were used in the extraction of peat swamp information in the past, and also solves the "salt and pepper phenomenon" and "enclave phenomenon" in the classified peat swamp spatial information, which does not have a clear issues of geographical significance. This embodiment has practical significance for the combination of optical images, radar images and DEM to quickly and automatically extract peat swamp information.

Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is: in step one, LandsatTM data is carried out to the preprocessing process as follows:

(1) Within the distribution range of the peat bog, determine the track number of the Landsat TM data of the peat bog, and download the Landsat TM data covering the distribution range of the peat bog according to the track number;

(2) In order to eliminate terrain distortion, the Landsat TM data is orthorectified by using the DEM data of the corresponding area of the Landsat TM data to obtain the Landsat TM data after orthorectification;

(3) In order to eliminate geometric distortion, use terrain data, select ground control points in ERDAS software, and perform geometric fine correction on Landsat TM data after orthorectification to obtain preprocessed Landsat TM data. Other steps and parameters are the same as those in Embodiment 1.

Specific implementation mode three: the difference between this implementation mode and specific implementation mode one or two is: in step 2, ENVISAT ASAR data is carried out preprocessing process:

(1) Within the coverage of the Landsat TM data range, download the ENVISAT ASAR fine image first-level data (ENVISAT ASAR APP Level 1B data) used in the test (the polarization mode is HH and HV);

(2) Carry out radiometric calibration on ENVISAT ASAR fine image first-level data, that is, convert the DN value of ENVISAT ASAR fine image first-level data into backscatter coefficient (in dB), and obtain radiometrically corrected ENVISAT ASAR data; its radiometric calibration The formula is as follows:

in, is the backscattering coefficient of the pixel in row i and column j; DN _ij is the original intensity value of the pixel in row i and column j; θ _ij is the radar incident angle of the pixel in row i and column j; K is is the absolute calibration coefficient;

(3) In order to eliminate terrain distortion, use the DEM data of the corresponding area of ENVISAT ASAR data to use the Range-Doppler imaging algorithm (Range-Doppler) to perform terrain correction on the radiation-corrected ENVISAT ASAR data;

(4) In order to eliminate image noise, the Enhanced Lee filter (window size 3╳3 pixels) was used to perform spatial filtering on ENVISAT ASAR data after terrain correction. Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

Embodiment 4: This embodiment differs from Embodiments 1 to 3 in that: in Step 4, the spatial registration error is controlled within 0.5 pixels. Other steps and parameters are the same as those in Embodiments 1 to 3.

Specific implementation mode five: the difference between this implementation mode and one of specific implementation modes one to four is that the optimal polarization mode band of the radar image extracted from the peat swamp in step seven is specifically: through statistics of different land cover types at the HV and HH poles The average value of the backscatter coefficients in the polarization mode is compared, and the difference between the backscatter coefficients is obvious as the optimal polarization mode band. Other steps and parameters are the same as in one of the specific embodiments 1 to 4.

Embodiment 6: This embodiment differs from Embodiment 1 to Embodiment 5 in that each segmentation unit in Step 8 is composed of pixels that are adjacent in space and have a homogeneity of 80% to 100%. Other steps and parameters are the same as one of the specific embodiments 1 to 5.

Specific embodiment seven: this embodiment is different from one of specific embodiments one to six in that: in step nine, utilize eCognition software to carry out the normalized normalized vegetation index (NDVI) and the normalized difference vegetation index (NDVI) that feature parameter extraction is carried out to a series of segmentation units that step eight obtains The National Water Index (NDWI) is:

Among them, TM2 is the second band of the Landsat TM sensor, TM3 is the third band of the Landsat TM sensor, and TM4 is the fourth band of the Landsat TM sensor. Other steps and parameters are the same as one of the specific embodiments 1 to 6.

Embodiment eight: the present embodiment is different from one of embodiment one to seven: the calculation formula of JM distance method (Jeffreys Matusita Distance) in step ten is as follows:

In the formula, i and j respectively represent any two different classification types; b _ij is the Bhattacharyya distance between the i-class classification type and the j-class classification type; m _i represents the mean vector of the i-class classification type, and m _j represents The mean value vector of category j; C _i represents the covariance matrix of category i, and C _j represents the covariance matrix of category j; the slope value (0°～42.43°), normalized to The vegetation index (NDVI) (-1～1), TM2+TM3-TM4-TM5 (-200.67～37.77) and hue (R:G:B=TM5:TM4:TM3) (0～1) are the participating classification bands . Other steps and parameters are the same as one of the specific embodiments 1 to 7.

Adopt the following examples to verify the beneficial effects of the present invention:

Embodiment one:

In this embodiment, a peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data is specifically prepared according to the following steps:

Step 1: Preprocessing Landsat TM data:

(1) Within the distribution range of the peat bog, determine the track number of the Landsat TM data of the peat bog, and download the Landsat TM data covering the distribution range of the peat bog according to the track number, the track number is P119R26, and the time is June 11, 2010;

(2) In order to eliminate terrain distortion, the Landsat TM data is orthorectified by using the DEM data of the corresponding area of the Landsat TM data to obtain the Landsat TM data after orthorectification;

(3) In order to eliminate geometric distortion, use terrain data, select ground control points in ERDAS software, and perform geometric fine correction on Landsat TM data after orthorectification to obtain preprocessed Landsat TM data;

Step 2: Preprocessing the ENVISAT ASAR data;

(1) Within the coverage of the Landsat TM data range, download the ENVISAT ASAR fine image first-level data (ENVISAT ASAR APP Level 1B data) used in the test (the polarization mode is HH and HV), and the time is July 2, 2010;

(2) Carry out radiometric calibration on ENVISAT ASAR fine image first-level data, that is, convert the DN value of ENVISAT ASAR fine image first-level data into backscatter coefficient (in dB), and obtain radiation-corrected ENVISAT ASAR data; its radiometric calibration The formula is as follows:

in, is the backscattering coefficient of the pixel in row i and column j; DN _ij is the original intensity value of the pixel in row i and column j; θ _i j is the radar incident angle of the pixel in row i and column j; K is the absolute calibration coefficient;

(3) In order to eliminate terrain distortion, in the NEXT 4C software, use the DEM data of the corresponding area of ENVISAT ASAR data to use the Range-Doppler imaging algorithm (Range-Doppler) to perform terrain correction on the radiation-corrected ENVISAT ASAR data;

(4) In order to eliminate image noise, apply the Enhanced Lee filter (window size 3×3 pixels) to perform spatial filtering on the ENVISAT ASAR data after terrain correction;

Step 3: Resample the ENVISAT ASAR data preprocessed in Step 2 in ArcGIS. The resampled ENVISAT ASAR data has the same grid size as the Landsat TM data processed in Step 1, and the grid size is 30m×30m;

Step 4: Based on the preprocessed Landsat TM data, compare the preprocessed Landsat TM data and the resampled ENVISAT ASAR data in the ArcGIS software, and use the function of adding control points provided by the Georeferencing module of the ArcGIS software Select the control points on the preprocessed Landsat TM data, and according to the ENVISAT ASAR data resampled by the spatial registration of the control points, the ENVISAT ASAR image is obtained; the spatial registration error is controlled within 0.5 pixels;

Step 5: Use the Aspect command in Surface Analysis under the Spatial Analyst module in ArcGIS software to extract the slope from the DEM data to obtain slope data;

Step 6: Based on the land cover type survey sample points, extract the backscatter coefficients of radar images in ArcGIS for different land cover types and different polarization modes from the ENVISAT ASAR image completed in step 4;

Step 7: Analyze the difference in radar backscatter coefficients between peat swamps and other different land cover types under different polarization modes, and determine the optimal polarization mode band for ENVISAT ASAR images, that is, the optimal polarization mode for radar images extracted from peat swamps band:

The average values of the backscatter coefficients of different land cover types under the HV and HH polarization modes are shown in Table 1:

Table 1

By comparison, it is found that the difference between the backscatter coefficients of peat bogs and other types of ground features under the HV polarization mode is more obvious than the difference between the backscatter coefficients of peat bogs and other types of ground features under the HH polarization mode. The best polarization mode band, so select the ENVISAT ASAR image HV polarization mode band. In addition, the backscattering coefficients of herbaceous swamps and peat bogs differ by 0.88dB under HH polarization mode, while the difference of backscattering coefficients between them is 3.44dB under HV polarization mode, so this study is suitable for studying herbaceous swamps;

Step 8: Use eCognition software to perform multi-layer and multi-scale segmentation on the preprocessed Landsat TM data, slope data, and the optimal polarization mode band of the ENVISAT ASAR image determined in step 7, to obtain a series of segmentation units, and use each segmentation unit as An object; each segmentation unit consists of spatially adjacent and homogeneous pixels of 80% to 100%. The parameter settings for multi-scale segmentation during object-oriented classification are shown in the table below:

Segmentation scale color factor form factor smoothness firmness 8 0.9 0.1 0.6 0.4

Step 9: Use eCognition software to extract the characteristic parameters of a series of segmentation units completed in step 8; wherein, the characteristic parameters include the average value of each band, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), TM2+TM3-TM4-TM5, Hue(R:G:B=TM5:TM4:TM3), Hue(R:G:B=TM4:TM3:TM2);

The normalized normalized vegetation index (NDVI) and normalized normalized water index (NDWI) for feature parameter extraction of a series of segmentation units are:

Among them, TM2 is the second band of the Landsat TM sensor, TM3 is the third band of the Landsat TM sensor, and TM4 is the fourth band of the Landsat TM sensor;

Step 10: According to the feature parameters extracted in step 9, use the JM distance (Jeffreys MatusitaDistance) method to determine the best classification band; the JM distance method (Jeffreys Matusita Distance) calculation formula is as follows:

In the formula, i and j respectively represent any two different classification types; b _ij is the Bhattacharyya distance between the i-class classification type and the j-class classification type; m _i represents the mean vector of the i-class classification type, and m _j represents The mean vector of the j category classification type; C _i represents the covariance matrix of the i category classification type, and C _j represents the covariance matrix of the j category classification type; the value range of the JM distance is between 0 and 2.0, when the value is greater than 1.9 It shows that the separability between ground features is good; according to the calculation results of JM, the slope value (0°～42.43°), normalized difference vegetation index (NDVI) (-1～1), TM2+TM3-TM4-TM5 (-200.67～37.77) and hue (R:G:B=TM5:TM4:TM3) (0～1) are bands involved in classification.

Step 11: According to the best classification band determined in step 10, refer to the survey sample points of land cover type, and use See5.0 software to establish a classification decision tree; among them, the reference land cover type survey sample points include peat swamp, herbaceous swamp, residential area , transportation land, farmland, forest land, water body and other land cover types;

Step 12: Run the classification decision tree in the eCognition software, export the land cover type classification results, and produce the land cover type vector file, the peat swamp extraction accuracy is 93%; among them, the land cover type vector file includes farmland, forest land, water body, residential Land cover types such as traffic land, herbaceous swamps and peat bogs;

Step 13: In the Layout View mode of the ArcGIS software, make a peat swamp thematic map based on the land cover type vector file completed in step 12 (the schematic diagram of the peat swamp thematic map is shown in Figure 2); that is, a ENVISAT-based Peat bog information extraction method from ASAR, Landsat TM and DEM data.

The present invention can also have other various embodiments, without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and deformations according to the present invention, but these corresponding changes and deformations are all Should belong to the scope of protection of the appended claims of the present invention.

Claims (7)

1. A peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data, characterized in that: Step 1: Preprocessing the Landsat TM data; Step 2: Preprocessing the ENVISAT ASAR data; Step 3: Resampling the ENVISAT ASAR data preprocessed in Step 2, the resampled ENVISATASAR data has the same grid size as the Landsat TM data processed in Step 1; Step 4: Use the function of adding control points provided by the Georeferencing module of ArcGIS software to select control points on the preprocessed Landsat TM data, and register the resampled ENVISAT ASAR data according to the control point space to obtain the ENVISAT ASAR image; Step 5: Extract the slope from the DEM data to obtain slope data; Step 6: Based on the land cover type survey sample points, extract the backscatter coefficients of radar images under different polarization modes for different land cover types from the ENVISAT ASAR image completed in step 4; Step 7: Analyze the difference in radar backscatter coefficients between peat swamps and other different land cover types under different polarization modes, and determine the optimal polarization mode band for ENVISAT ASAR images, that is, the optimal polarization mode for radar images extracted from peat swamps band; Step 8: Carry out multi-layer and multi-scale segmentation on the preprocessed Landsat TM data, slope data and the optimal polarization mode band of the ENVISAT ASAR image determined in step 7 to obtain a series of segmentation units; Step 9: Extract feature parameters from a series of segmentation units completed in step 8; wherein, the feature parameters include the average value of each band, normalized vegetation index, normalized water index, TM2+TM3-TM4-TM5 and hue ; Wherein, TM2 is the 2nd band of the Landsat TM sensor, TM3 is the 3rd band of the Landsat TM sensor, and TM4 is the 4th band of the Landsat TM sensor; Step ten: according to the feature parameter extracted in step nine, utilize the JM distance method to determine the best classification band; The calculation formula of JM distance method is as follows: <mrow><msub><mi>JM</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><mo>=</mo><msqrt><mrow><mn>2</mn><mrow><mo>(</mo><mn>1</mn><mo>-</mo><msup><mi>e</mi><mrow><mo>-</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub></mrow></msup><mo>)</mo></mrow></mrow></msqrt><mo>;</mo></mrow> <mrow><msub><mi>b</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><mo>=</mo><mn>1</mn><mo>/</mo><mn>8</mn><msup><mrow><mo>(</mo><msub><mi>m</mi><mi>i</mi></msub><mo>-</mo><msub><mi>m</mi><mi>j</mi></msub><mo>)</mo></mrow><mi>T</mi></msup><mfrac><mrow><msub><mi>c</mi><mi>i</mi></msub><mo>+</mo><msub><mi>c</mi><mi>j</mi></msub></mrow><mn>2</mn></mfrac><mrow><mo>(</mo><msub><mi>m</mi><mi>i</mi></msub><mo>-</mo><msub><mi>m</mi><mi>j</mi></msub><mo>)</mo></mrow><mo>+</mo><mn>1</mn><mo>/</mo><mn>2</mn><mi>ln</mi><mfrac><mrow><mo>|</mo><mrow><mo>(</mo><msub><mi>c</mi><mi>i</mo>mi></msub><mo>-</mo><msub><mi>c</mi><mi>j</mi></msub><mo>)</mo></mrow><mo>/</mo><mn>2</mn><mo>|</mo></mrow><mrow><msup><mrow><mo>|</mo><msub><mi>c</mi><mi>i</mi></msub><mo>|</mo></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msup><msup><mrow><mo>|</mo><msub><mi>c</mi><mi>j</mi></msub><mo>|</mo></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msup></mrow></mfrac><mo>;</mo></mrow> In the formula, i and j represent any two different classification types; b _ij is the Bhattachary distance between the i-class classification type and the j-class classification type; m _i represents the mean vector of the i-class classification type, and m _j represents the j-class classification The mean vector of the type; C _i represents the covariance matrix of the i-class classification type, and C _j represents the covariance matrix of the j-class classification type; Step 11: According to the optimal classification band determined in step 10, a classification decision tree is established with reference to the survey sample points of land cover types; among them, the survey sample points of reference land cover types include peat swamp, herbaceous swamp, residential area, traffic land, farmland , forest land, water body land cover type; Step 12: Run the classification decision tree, export the land cover type classification results, and produce the land cover type vector file; where the land cover type vector file includes farmland, forest land, water body, residential traffic land, herbaceous swamp and peat swamp land cover types ; Step 13: Make a peat swamp thematic map based on the land cover type vector file completed in step 12; that is, a peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data is completed. 2. A kind of peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data according to claim 1, is characterized in that: in step 1, Landsat TM data is carried out to preprocessing process as: (1) Within the distribution range of the peat bog, determine the track number of the Landsat TM data of the peat bog, and download the Landsat TM data covering the distribution range of the peat bog according to the track number; (2) Use the DEM data of the corresponding area of the Landsat TM data to perform orthorectification on the Landsat TM data, and obtain the Landsat TM data after orthorectification; (3) Using terrain data, select ground control points in ERDAS software, and perform geometric fine correction on the orthorectified Landsat TM data to obtain the preprocessed Landsat TM data. 3. A kind of peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data according to claim 1, is characterized in that: in step 2, ENVISAT ASAR data is carried out preprocessing process: (1) Within the coverage of Landsat TM data range, download ENVISAT ASAR fine image level 1 data; (2) Carry out radiometric calibration on ENVISAT ASAR fine image first-level data, that is, transform the DN value of ENVISAT ASAR fine image first-level data into backscatter coefficient, and obtain radiometrically corrected ENVISAT ASAR data; the radiometric calibration formula is as follows: <mrow><msubsup><mi>&amp;sigma;</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>0</mn></msubsup><mo>=</mo><mn>10</mn><mo>&amp;CenterDot;</mo><msub><mi>log</mi><mn>10</mn></msub><mo>&amp;lsqb;</mo><mfrac><mrow><msubsup><mi>DN</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>2</mn></msubsup></mrow><mi>K</mi></mfrac><mi>s</mi><mi>i</mi><mi>n</mi><mrow><mo>(</mo><msub><mi>&amp;theta;</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><mo>)</mo></mrow><mo>&amp;rsqb;</mo></mrow> in, is the backscattering coefficient of the pixel in row i and column j; DN _ij is the original intensity value of the pixel in row i and column j; θ _ij is the radar incident angle of the pixel in row i and column j; K is is the absolute calibration coefficient; (3) Using the DEM data of the area corresponding to the ENVISAT ASAR data, the range Doppler imaging algorithm is used to perform terrain correction on the radiometrically corrected ENVISAT ASAR data; (4) Apply the Enhanced Lee filter to perform spatial filtering on ENVISAT ASAR data after terrain correction. 4. A peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data according to claim 1, characterized in that in step 4, the spatial registration error is controlled within 0.5 pixels. 5. A method for extracting peat swamp information based on ENVISAT ASAR, Landsat TM and DEM data according to claim 1, characterized in that: in step 7, the optimal polarization mode band of the radar image for peat swamp extraction is specifically: by Statistically compare the average values of the backscatter coefficients of different land cover types under the HV and HH polarization modes, and compare the difference between the backscatter coefficients as the best polarization band. 6. A kind of peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data according to claim 1, is characterized in that: in step 8, each segmentation unit reaches 80%～by spatially adjacent and homogeneity 100% composed of pixels. 7. A kind of peat swamp information extraction method based on ENVISAT ASAR, Landsat TM and DEM data according to claim 1, is characterized in that: in step 9, a series of segmentation units obtained in step 8 are carried out to the normalization of feature parameter extraction The vegetation index NDVI and normalized water index NDWI are: <mrow><mi>N</mi><mi>D</mi><mi>V</mi><mi>I</mi><mo>=</mo><mfrac><mrow><mi>T</mi><mi>M</mi><mn>4</mn><mo>-</mo><mi>T</mi><mi>M</mi><mn>3</mn></mrow><mrow><mi>T</mi><mi>M</mi><mn>4</mn><mo>+</mo><mi>T</mi><mi>M</mi><mn>3</mn></mrow></mfrac><mo>,</mo><mi>N</mi><mi>D</mi><mi>W</mi><mi>I</mi><mo>=</mo><mfrac><mrow><mi>T</mi><mi>M</mi><mn>2</mn><mo>-</mo><mi>T</mi><mi>M</mi><mn>4</mn></mrow><mrow><mi>T</mi><mi>M</mi><mn>2</mn><mo>+</mo><mi>T</mi><mi>M</mi><mn>4</mn></mrow></mfrac><mo>.</mo></mrow> 2