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Image Processing for Remote Sensing - Chapter 7

Vâng, bạn cam xa Hyperspectral khai Thác phòng ngủ Hinh mo cua tan xa Điền bạn thuế GTGT phòng ngủ nơi Khắc nhau bài hát Step o cu [2,3]. Hyperspectral cũng cam You Been phát triển Je đặt pho mau cua phan phan Điền bạn rong tan MB Khu Vực Co bạn Bốn Nhin thay Thong Ngoai hong hong gan sẽ Ngoai trung, trong xe điện treo tiếp giáp dai hep [4,5]. Phòng ngủ SW lương seventh Dang dan nang sẽ Đa Quán Sứ được biết đến ứng phân vien Thám để hyperspectral lon cua IS . | 7 Vertex Component Analysis A Geometric-Based Approach to Unmix Hyperspectral Data Jose M.B. Dias and Jose M.P. Nascimento CONTENTS 7.1 Introduction.149 7.2 Vertex Component Analysis Algorithm.151 7.2.1 Subspace Estimation.152 7.2.1.1 Subspace Estimation Method Description.153 7.2.1.2 Computer Simulations.155 7.2.2 Dimensionality Reduction. 157 7.2.3 VCA Algorithm.158 7.3 Evaluation of the VCA Algorithm.160 7.4 Evaluation with Experimental Data.166 7.5 Conclusions.168 References . 170 7.1 Introduction Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths 2 3 . Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared in hundreds of narrow contiguous bands 4 5 . The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous 6 7 . Very often the resolution cell corresponding to a single pixel in an image contains several substances endmembers 4 . In this situation the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra called endmember signatures and the corresponding abundance fractions 8-10 . Depending on the mixing scales at each pixel the observed mixture is either linear or nonlinear 11 12 . Linear mixing model holds approximately when the mixing scale is macroscopic 13 and there is negligible interaction among distinct endmembers 3 14 . If however the mixing scale is microscopic or intimate mixtures 15 16 and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers 17 the linear model is no longer accurate. Work partially based on 1 . 149 2008 by Taylor Francis Group LLC .