Papers › BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for...

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

1 Dec 2016arXiv:1612.00144archive 2025-07-28

Anirban Santara, Kaustubh Mani, Pranoot Hatwar, Ankit Singh, Ankur Garg, Kirti Padia, Pabitra Mitra

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

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kaustubh0mani/BASS-Net officialmentioned in papermentioned on GitHubtorch report

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Tasks

Classification Deep Learning General Classification Hyperspectral Image Classification Image Classification image-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Hyperspectral Image Classification	Indian Pines	BASSNet	Overall Accuracy	96.77%	#31 of 34	Archive leaderboard	report
Hyperspectral Image Classification	Pavia University	BASSNet	Overall Accuracy	97.48%	#31 of 33	Archive leaderboard	report

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