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HYPERSPECTRAL IMAGE CLASSIFICATION USING SPECTRAL-SPATIAL CONVOLUTIONAL NEURAL NETWORKS
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DOI：10.1109/IGARSS39084.2020.9323392.png)
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摘要
En 中文
Hyperspectral images provide detailed information about the scanned objects, as they capture their spectral characteristics within a large number of wavelength bands. Classification of such data has become an active research topic due to its wide applicability. In this paper, we introduce a new spectral-spatial convolutional neural network, benefitting from a battery of data augmentation techniques which help deal with a real-life problem of lacking ground-truth training data. Our experiments showed that the proposed method works in real time and outperforms other spectral-spatial algorithms.
Keyword:
Hyperspectral imaging
deep learning
classification
segmentation
3D convolution
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