A case study on the urban impervious surface distribution based on a BCI index

Xiaolin Chen; Genyun Sun; Zhenjie Wang

doi:10.1117/12.2209434

17 December 2015 A case study on the urban impervious surface distribution based on a BCI index

Xiaolin Chen, Genyun Sun, Zhenjie Wang

Proceedings Volume 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis; 981117 (2015) https://doi.org/10.1117/12.2209434
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Endmember selection is the key to success in pixel unmixing which plays an important role in urban impervious surface abundance extraction. During the extraction, however, there has been a problem for the discrimination of impervious surfaces and soils because of their similarity in spectral. This increase the difficulty in distinguishes impervious surface and soil in endmember selection. To address this issue, in the current study, the biophysical composition index (BCI) and soil adjusted vegetation index (SAVI) were introduced to enhance the information of impervious surface and bare soil in the study area. Then, by selecting high albedo, low albedo, soil and vegetation endmembers with the utilization of the histogram of the indices and minimum noise fraction (MNF) scatter plot, we applied spectral mixture analysis (SMA) to extract impervious surface abundance. The scene of multispectral Landsat TM image was acquired allowing for the interpretation and analysis of impervious surfaces distribution. Experiments and comparisons indicate that this method performs well in estimating subpixel impervious surface distribution with relatively high precision and small bias.

Citation Download Citation

Xiaolin Chen, Genyun Sun, and Zhenjie Wang "A case study on the urban impervious surface distribution based on a BCI index", Proc. SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 981117 (17 December 2015); https://doi.org/10.1117/12.2209434

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KEYWORDS

Shape memory alloys

Brain-machine interfaces

Vegetation

Soil science

Earth observing sensors

Image enhancement

Landsat

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