Incorporating Spectral Unmixing in Satellite Imagery Semantic Segmentation

Abstract

Land-cover classification to distinguish physical covers of Earth's surface is one of the critical tasks in remote sensing. Although deep learning-based approaches have shown remarkable performance in semantic segmentation, they require a massive amount of training data. Thus, the generalization capability of these approaches is of great importance, especially in working with satellite images when the amount of available labeled data is quite limited. In this paper, we propose incorporating spectral unmixing methods to obtain powerful representations of spectral information for semantic segmentation of satellite images. We show that land-cover classification performance can be enhanced by this proper extraction of features as input to the deep learning-based model. The experimental results demonstrate promising potential improvements in terms of segmentation accuracy. In addition, qualitative assessments show a higher confidence level of the proposed framework in predicting a label for a given pixel.