Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters |
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| Affiliation: | 1. School of Geography, South China Normal University, Guangzhou 510631, China;2. Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI 53201-0413, United States;1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100094, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;4. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Science and Natural Resources Research of Chinese Academy of Science, Beijing 100101, China;1. State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China;2. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China;3. Center for Human-Environment System Sustainability (CHESS), Beijing Normal University, Beijing 100875, China;4. Pacific Northwest National Laboratory, 5825 University Research Court, Suite 3500, College Park, MD 20740, USA;1. Department of Geography, State University of New York at Binghamton, P.O. Box 6000, Binghamton, NY 13902, United States;2. Department of Geosciences, Texas Tech University, Lubbock, TX 79409, United States;1. Department of Geography and Environmental Studies, Stellenbosch University, Stellenbosch, South Africa;2. Department of Environmental Sciences, Rhodes University, Grahamstown, South Africa;3. Agricultural Research Council-Animal Production Institute, Grahamstown, 6140, South Africa |
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| Abstract: | Successful retrieval of urban impervious surface area is achieved with remote sensing data using the multiple endmember spectral mixture analysis (MESMA). MESMA is well suited for studying the urban impervious surface area because it allows the number and types of the endmembers to vary on a per-pixel basis, thereby, allowing the control of the large spectral variability. However, MESMA must calculate all potential endmember combinations of each pixel to determine the best-fit one. Therefore, it is a time-consuming and inefficient unmixing technology, especially for hyperspectral images because these images have more complicated endmember categories. Hence, in this paper, we design an improved MESMA (SASD-MESMA: spectral angle and spectral distance MESMA) to enhance the computational efficiency of conventional MESMA, and we validate this new method by analyzing the Hyperion image (Jan-2011) and the field-spectra data of Guangzhou (China). In SASD-MESMA, the parameters of spectral angle (SA) and spectral distance (SD) are used to evaluate the similarity degree between library spectra and image spectra in order to identify the most representative endmember combination for each pixel. Results demonstrate that the SA and SD parameters are useful to reduce misjudgment in selecting candidate endmembers and effective for determining the appropriate endmembers in one pixel. Meanwhile, this research indicates that the proposed SASD-MESMA performs very well in retrieving impervious surface area, forest, grass and soil distributions on the sub-pixel level (the overall root mean square error (RMSE) is 0.15 and the correlation coefficient of determination (R2) is 0.68). |
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| Keywords: | MESMA Spectral angle Spectral distance Endmember selection Impervious surface area |
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