HSIS-Net: Hyperspectral Image Segmentation Using Multi-view Active Learning Based FCSN

Abstract

Hyperspectral image (HSI) segmentation and classification is trending research in military and civil applications area. However, HSI classification is facing various challenges in analyzing spectral and spatial regions. In order to improve the performance of HSI classification models, segmentation is essential step. Therefore, this article is focused on implementation of unified HSI segmentation network (HSIS-Net) using active learning. Initially, HSI preprocessing operation is performed to normalize the spectral-spatial regions. Then, joint spatial-spectral boundary extraction operation is performed using spatial information divergence (SID) and spectral correlation mapper (SCM). Finally, segmentation of boundary estimated HSI bands is performed using multi-view active learning network based fully convolutional segmentation network (MAL-FCSN). The simulations revealed that the proposed HSIS-Net resulted in superior segmentation performance with segmentation accuracy (SA) of 0.999, and segmentation F1-score of 0.999 as compared to the existing HSI classification approaches for four publicly available HSI datasets.