A novel bilinear unmixing approach for reconsideration of subpixel classification of land cover

Abstract

Hyperspectral mixing depends on a variety of factors such as the instantaneous field of view of observation device, the height of the image capturing platform from the scene, the properties of materials in the pixel, and the interaction structure of these materials with incident light in the scene. Different unmixing models have been considered to model the hyperspectral mixing. The simplest one is the linear mixing model. Nevertheless, it has been recognized that mixing phenomena is usually nonlinear. Bilinear and linear–quadratic models have become popular recently, and also the bilinear polynomial post-nonlinear model shows promising results. Most of these nonlinear models consider only the reflection interaction. However, especially in regions like vegetated areas, absorbance and transmittance are also important facts which affect the mixture. So, they should be taken into account when dealing with physics of hyperspectral mixing. In this study, an enhanced bilinear mixing model is proposed for analyzing the physics of hyperspectral mixing. The model takes into account the transmittance, absorption and reflection. The results show that our enhanced bilinear viewpoint is superior, in terms of pixel reconstruction error, when compared to that of linear and other bilinear models which consider only the reflection interaction.