Classification Method Validation for Rice Mapping Using ENVISAT APS Data Erxue CHEN(1), Zengyuan LI(1), BingxiangTan(1)，Wei He(1), Bingbai LI(2) (1)Institute of Forest Resources Information Techniques, Chinese Academy.
Download ReportTranscript Classification Method Validation for Rice Mapping Using ENVISAT APS Data Erxue CHEN(1), Zengyuan LI(1), BingxiangTan(1)，Wei He(1), Bingbai LI(2) (1)Institute of Forest Resources Information Techniques, Chinese Academy.
Classification Method Validation for Rice Mapping Using ENVISAT APS Data Erxue CHEN(1), Zengyuan LI(1), BingxiangTan(1)，Wei He(1), Bingbai LI(2) (1)Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, 100091, Beijing, PRC. China (2) Institute of Agriculture Modernization, Jiangsu Academy of Agriculture Sciences, 210014, Nanjing, PRC. China [email protected], [email protected], [email protected], [email protected] Outlines Introduction; Test site and EO data; ASAR APS data pre-processing; Classification evaluation methods; Results and analysis; Conclusions. Introduction • Regular monitoring of the crop is necessary in the major rice producing countries for political, economical and food security reasons; • We validated two kinds of APS preprocessing schemes and compared their classification performance. Test site and EO data • Xinghua district of Jiangsu Province, PRC. China Test site location on China map •Ground true data Landsat TM image. R: b 5; G: b4; B: b3 Ground true map generated from Landsat TM image interpretation and aided by field work •ENVISAT ASAR APS data used ASAR APS data pre-processing • Backscattering coefficient based method (BSCBM ); • Polarimetric SAR data processing method (PSDPM ). •BSCBM complex T1 T2 T3 Radiometric calibration Intensity BSC1 BSC2 i0, j DN i2, j sin j K 1 R 4j sin ref G j 2 Rref BSC3 Multi-look (6az*2rg) Spatial speckle filtering Lee speckle filter Geocoded ellipsoid correction Range-doppler Model SAR to TM image co-registration db BSC1 BSC2 MLC BSC3 INPUTS •PSDPM Scattering matrix S11 S S 21 Scattering vector VV+VH S12 S 22 0 S S 21 VV+VH S12 S 22 Covariance matrix (multi-looked) kp S12 S 22 [C ] k p k * p S12 2 C11 C12 * C C 22 21 S12 S 22 * S12 S 22 2 S 22 Lee Polarimetric Filter H-Alpha decomposition Entropy or H Alpha CMF S12 2 * S12 S 22 Intensity-VH * S12 S 22 2 S 22 Multi-looked and filtered [C] MLC Intensity-VV INPUTS POLSARPro©ESA used for all these processing routes. Classification evaluation methods •Dataset inputs to the MLC 1.BSCBM • 6BSC-(3VV+3VH): All 6 BSC images from three dates; • 3BSC-VV: the 3 VV polarization BSC images from three dates; • 3BSC-VH: the 3 VH polarization BSC images from three dates. 2.PSDPM • 6I-(3VV+3VH): All the 6 intensity images from three dates; • 3I-VV: the 3 VV polarization intensity images from three dates; • 3I-VH: the 3 VH polarization intensity images from three dates; • 12IAE-(6I+3A+3E): 6 intensity and 3 alpha and 3 entropy images. • 6AE-(3A+3E): All the 3 alpha and entropy images. 1.BSCBM Multi-temporal VV BSC image Multi-temporal VH BSC image 2.PSDPM Multi-temporal VV intensity Image Multi-temporal VH Intensity image 2.PSDPM Polarimetric alpha image Polarimetric entropy image •Training and test samples collected from ground true data Results and Analysis •PSDPM compared with BSCBM classification performance of PSDPM is better than BSCBM Results and Analysis •VV compared with VH • The classification accuracy of VV is better than VH. •The contribution of Entropy and Alpha • Intensity combined with E+Alpha leads to lower classification accuracy，but highest rice acc.; • E+Alpha only achieved the worst performance Conclusions (1)For rice mapping using ENVISAT ASAR APS level-1 data, it has been observed that polarimetric data processing method is better than backscattering coefficient based processing method; (2)Multi-temporal VV co-polarization SAR data has higher capability for land cover classification in agricultural region than multi-temporal VH crosspolarization SAR data in C band; (3) Applying PSDPM to APS data and combining all the information from it as inputs to a certain classifier was suggested for operational rice mapping. ACKNOWLEDGES Thanks for European Space Agency to provide all the ENVISAT ASAR datasets used in this study. Many thanks towards Dr. Mike Wooding of RSAC of UK. for helps to order the ASAR APS data from ESRIN. Thanks!