Endmembers extraction using time series of MODIS and TM samples

Kaiwen Zhong D.V.M.; Xulong Liu; Wanxia Liu

doi:10.1117/12.746808

14 November 2007 Endmembers extraction using time series of MODIS and TM samples

Kaiwen Zhong D.V.M., Xulong Liu, Wanxia Liu

Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 67900R (2007) https://doi.org/10.1117/12.746808
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Low spatial resolution imagery is one of the important remote sensing data sources for remote sensing monitoring of a large scope and distribution of vegetation. There exist many mixed pixels in low spatial resolution image, which need an effective method to deal with them and to be improved the quality of classification images. In this paper, linear mixing model is used to unmix the time series of MODIS-NDVI data. The endmembers extraction is a key and necessity, which represents the spectral characteristics of the single pure land cover types. A new endmembers extraction algorithm based on the time series of MODIS-NDVI and TM sample data is presented in this paper.Using these methods, we evaluate the clarification results and find wheat distribution's region accuracy and pixel accuracy reach to 92.9% and 0.837 respectively, which are higher than the clarification result based on the endmembers from MODIS-NDVI pixel purity index analysis or from classifications of TM data. This shows that our endmembers extraction algorithm is available and effective, which help to improve monitoring accuracy of large scope and distribution of vegetation.

Citation Download Citation

Kaiwen Zhong D.V.M., Xulong Liu, and Wanxia Liu "Endmembers extraction using time series of MODIS and TM samples", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67900R (14 November 2007); https://doi.org/10.1117/12.746808

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KEYWORDS

MODIS

Vegetation

Data modeling

Remote sensing

Image quality

Spatial resolution

Statistical analysis

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