Unsupervised Nonlinear Spectral Unmixing of Satellite Images Using the Modified Bilinear Model

Abstract

Episodes of mixing pixels in satellite imageries are more prevalent. Hence, spectral unmixing approach is used to perform the sub-pixel classification of satellite images. Many unmixing works were done based on the assumption that the pixels are linearly mixed (single interaction) but in real scenarios, the pixels are nonlinearly mixed due to interactions. Fan model and generalized bilinear model consider only the bilinear interactions for nonlinear unmixing. In reality, multiple interactions between the various classes are also present in the image. In this work, a new model, ‘modified bilinear model’ is proposed to perform the nonlinear unmixing process that considers the entire single, bilinear and multiple interactions into account. This system adaptively changes the mixing model on per pixel basis depending on the nonlinearity parameter. It has been tested with the multispectral, synthetic and real hyperspectral datasets and also illustrated notable advantages compared with the other methods.