BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

• Published in: IEEE transactions on geoscience and remote sensing Vol. 55; no. 9; pp. 5293 - 5301
• Main Authors: Santara, Anirban, Mani, Kaustubh, Hatwar, Pranoot, Singh, Ankit, Garg, Ankur, Padia, Kirti, Mitra, Pabitra
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Architecture; Classification; Convolutional neural network (CNN); deep learning; Feature extraction; hyperspectral imagery; Hyperspectral imaging; Image classification; Land cover; landcover classification; Machine learning; Neural networks; pattern classification; Spatial discrimination learning; Spatial variations; Spectra; Spectral signatures; Support vector machines; Training
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2017.2705073

Deep learning based land cover classification algorithms have recently been proposed in the literature. In hyperspectral images (HSIs), they face the challenges of large dimensionality, spatial variability of spectral signatures, and scarcity of labeled data. In this paper, we propose an end-to-end deep learning architecture that extracts band specific spectral-spatial features and performs land cover classification. The architecture has fewer independent connection weights and thus requires fewer training samples. The method is found to outperform the highest reported accuracies on popular HSI data sets.