Abstract
Hyperspectral (HS) images are highly accurate for mineral discrimination, but available areas are limited. For this reason, several methods have been proposed to extend the mineral map of the overlap region between HS and multispectral (MS) images to the surrounding area with no HS image. One such method, proposed by Hirai and Tonooka, discriminates minerals using MS images by obtaining the endmembers of MS images from the positions of the endmember pixels of HS images in the overlap region. While this method (referred to as HT method) has the advantage of being less susceptible to the spectral distortions of HS and MS images, it also has the problem of reduced accuracy due to misalignment between HS and MS images. We proposed an improved HT method that reduces the effects of the above problems by incorporating a process that improves the robustness against misalignment by searching for the best MS endmember pixel around the position of the HS endmember pixel and a process that determines more optimum threshold value of each mineral in the spectral angle mapper method used in the HT method. As a result of evaluation using an AVIRIS image as an HS image and a World View-3 image as an MS image at Cuprite, Nevada, the improved method improved the overall accuracy by 2.6% compared with the original HT method, and in the case that the HS and the MS images were misaligned, the overall accuracy of the original method decreased by 7.0%, while the improved method decreased by only 1.5%. These results indicate that the improved method can perform as expected.
Highlights
Multispectral (MS) images are useful for mineral discrimination by remote sensing, some minerals may be difficult to discriminate due to limitations caused by the small number of bands and wide band widths.[1]
As countermeasures to these issues, we propose to apply the following two improvements to the HT method: (1) the MS-based endmember pixels are selected not from only the position of each endmember pixel in the HS image and from the neighboring region of that based on the overall accuracy which can be calculated from provisional mapping results by the spectral angle mapper (SAM) method, and (2) the optimum threshold of the SAM method is selected not manually but by automatic search based on the overall accuracy
[Step 4] For each mineral, the individual map of only the mineral is temporally generated from the MS image for each MS-based endmember candidate and its neighboring pixels (e.g., 5 × 5, 7 × 7 pixels) using the SAM method, and the overall accuracy is calculated by comparing with the individual mineral map of the mineral which is extracted from the HS-based mineral map generated in step 2
Summary
Multispectral (MS) images are useful for mineral discrimination by remote sensing, some minerals may be difficult to discriminate due to limitations caused by the small number of bands and wide band widths.[1]. Because Kruse and Perry’s method (referred to as KP method) can degrade due to the influence of spectral distortions caused by calibration errors, atmospheric correction errors etc., Hirai and Tonooka proposed an alternate method that classifies minerals from the MS image using MS-based endmember pixels extracted based on the locations of endmember pixels in the HS image.[13] As a result of evaluation using AVIRIS and ASTER/SWIR images in the Cuprite region, Hirai and Tonooka’s method (referred to as HT method) showed higher accuracy than KP method for data with spectral distortions, but the registration error between HS and MS images caused a decrease in accuracy. In this study, we propose an improved HT method that improves the robustness against registration errors and optimizes the threshold of each mineral used in the spectral angle mapper (SAM) method[14] included as a part of the HT method
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