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BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Sep2017, Vol. 55 Issue 9, p5293-5301, 9p
- Publication Year :
- 2017
-
Abstract
- 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 55
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 125755551
- Full Text :
- https://doi.org/10.1109/TGRS.2017.2705073