A Novel Hyperspectra Absorption Enhancing Method Based on Morphological Top-Hat Transformation
LI Hui1, 2, 3, LIN Qi-zhong1, 2, WANG Qin-jun1, 2, LIU Qing-jie1, 2, CHEN Yu1, 3
1. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100086, China 2. Key Laboratory of Digital Earth Sciences, Chinese Academy of Sciences, Beijing 100086, China 3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：Hyperspectral characteristics analysis of ground features is the basis for applications of high-resolution imaging technology to ground target identification and ground features classification. Based on morphological multi-scale Top-Hat transformation, a novel spectral absorption enhancing algorithms was put forward, which enhanced spectral absorption features while maintaining shape features of the absorption peak bands. Eleven reflectance spectra of different mineral groups were chosen from the mineral spectral library of the United States Geological Survey (USGS), and we used a K-means clustering analysis on both the absorption-enhanced spectra and the original reflectance spectra. Results showed that, firstly, clustering groups of the absorption-enhanced spectra (AES) had better similarity within the same clustering group, and greater difference between different groups, furthermore, they were more consistent with the geological background of these minerals compared with clustering result of the original spectra (OS). Secondly, while all the original spectra were re-sampled to their ASTER spectra and the AES clustering result was displayed in the form of ASTER spectra of the minerals, we could easily describe both the representative spectral feature of each clustering group, and the typical spectral differences between every two groups. These fully demonstrate that the absorption-enhanced spectra have enhanced absorption features of the mineral spectra, and improved the separability of hyper-spectra. Accordingly, feature analysis based on absorption enhanced spectra can be used as reference for information extracting based on multi-spectral remote sensing image data, and it is a very useful method of hyperspectral analysis.
[1] Graham R Hunt. Electromagnetic Radiation: The communication link in Remote Sensing in Siegal B S，Gillespie A R，eds. New York：John Wiley & Sons, 1980, (2): 5. [2] Graham R Hunt, Roger P Ashley. Economic Geology, 1979, 74: 1613. [3] ZHAO Ying-shi(赵英时). Principles and Methods of Analysis of Remote Sensing Applications(遥感应用分析原理与方法). Beijing：Science Press(北京：科学出版社), 2000. [4] PU Rui-liang，GONG Peng(浦瑞良，宫 鹏). Hyperspectral Remote Sensing and Its Applications(高光谱遥感及其应用). Beijing：Higher Education Press(北京：高等教育出版社)，2003. [5] CHEN Wen-xia, CHEN An-sheng, CAI Zhi-hua(陈文霞，陈安升，蔡之华). Computer Engineering and Applications(计算机工程与应用), 2008, 44(28): 230. [6] CUI Yi(崔 屹). Image Processing and Analysis-Mathematical Morphology Methods and Applications(图像处理与分析-数学形态学方法及应用). Beijing：Science Press(北京：科学出版社)，2000. [7] Meyer F S. Signal Processing, 1989, 16(4): 3032317. [8] Soille P. A Note on Morphological Contrast Enhancement. Technical Report RT-PS-001. Ecole des Mines of Ales-EERIE. 1997. [9] San Fandila-Pedro Escobedo. Advances in Artificial Intelligence, 2004, 2972: 662. [10] Mukhopadhyay S, Chanda B. Signal Processing, 200，80(4): 685. [11] XIU Lian-cun, ZHENG Zhi-zhong, YU Zheng-kui, et al(修连存，郑志忠，愈正奎，等). Acta Geologica Sinica(地质学报), 2007, 81(11): 1585. [12] YAN Shou-xun, ZHANG Bing, ZHAO Yong-chao, et al(燕守勋, 张 兵，赵永超，等). Remote Sensing Technology and Application(遥感技术与应用)，2003, 18(4): 191.
