Semi-Supervised Semantic Segmentation of Hyper-Spectral Images

Shankho Subhra Pal, Jayanta Mukhopadhyay, Sudeshna Sarkar

Abstract: Hyper-spectral images have hundreds of bands, which provide rich information compared to popular RGB images. This information can be leveraged to accurately segment images into multiple fine-grained classes. However, processing high-dimensional data poses challenges. In this work, we propose two methods for land cover classification using hyper-spectral images, aiming to achieve accurate classification into multiple fine-grained classes. One of the main challenges in land cover classification is the availability of labeled data. To address this, we employ semi-supervised methods that require less labeled data and effectively utilize the vast amount of un-labeled data. We evaluate our methods on the Indian Pine dataset, while varying the amount of labeled pixels used for training.