Automatic Cloud Detection in Resourcesat LISS-3 Data using Spectral Angle Similarity and Mode Seeking Reference Spectra

Optical remote sensing images contain cloud pixels, which need to be detected and masked for different earth observation studies and applications. In literature, various algorithms are reported which can detect cloud in multi-spectral remote sensing images. In this paper, we have developed an approach based on spectral angle computation that measures the radiometric gap between a reference pixel and input image pixels. Reference spectra is determined using mode seeking technique by taking multiple cloud pixels from multi-temporal images covering different land terrain. The processing chain is developed and tested in Resourcesat LISS-3 surface reflectance data. Result section shows that technique developed can determine thick cloud pixels with high confidence interval and compared with other novel cloud detection methods.