Journal
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
Volume 91, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jag.2020.102157
Keywords
features extraction; hyperspectral image (HSI) classification; convolutional neural network (CNN); deep learning
Categories
Funding
- Chinese National Natural Science Foundation [41971292, 41801275, 41871270]
- Guangdong Polytechnic Normal University (GPNU), Innovation Team Project of the Education Department of Guangdong Province [2017KCXTD021]
- Key Laboratory of the Education Department of Guangdong Province, China [2019KSYS009]
Ask authors/readers for more resources
This study presents a deep extraction of localized spectral features and multi-scale spatial features convolution (LSMSC) framework for spectral-spatial fusion based classification of hyperspectral images (HSIs). First, adjacent spectral bands are grouped based on their similarity measurements, where the whole hypercube is partitioned into several sub-cubes, each corresponding to one band group. Then, the proposed localized spectral features extraction (LSF) strategy is used to extract localized spectral features, which are extracted from each band group using the 1D convolutional neural network (CNN). Meanwhile, the proposed HiASPP strategy is employed to extract the multi-scale features from the first several principal components of each sub-cube. Finally, the extracted spectral and spatial features are concatenated for spectral-spatial fusion based classification of HSI. Experiments conducted on three publicly available datasets have demonstrated that the proposed architecture outperforms several state-of-the-art approaches.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available