Hyperspectral image sensors capture surface reflectance over a range of wavelengths. The
fine spectral information is recorded in terms of hundreds of bands. Hyperspectral image …

Due to its impressive representation power, the graph convolutional network (GCN) has
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …

Deep learning has achieved great success in hyperspectral image classification. However,
when processing new hyperspectral images, the existing deep learning models must be …

The growth of Hyperspectral Image (HSI) analysis is due to technology advancements that
enable cameras to collect hundreds of continuous spectral information of each pixel in an …

Deep learning, which represents data by a hierarchical network, has proven to be efficient in
computer vision. To investigate the effect of deep features in hyperspectral image (HSI) …

Due to its simple, fast, and good generalization ability, extreme learning machine (ELM) has
recently drawn increasing attention in the pattern recognition and machine learning fields …

Hyperspectral remote sensing has a strong ability in information expression, so it provides
better support for classification. The methods proposed to deal the hyperspectral data …

Kernel methods, eg, composite kernels (CKs) and spatial-spectral kernels (SSKs), have
been demonstrated to be an effective way to exploit the spatial-spectral information …

Accurate land cover mapping is important for urban planning and management. Remote
sensing data have been widely applied for urban land cover mapping. However, obtaining …

This paper proposes a spectral-spatial feature learning (SSFL) method to obtain robust
features of hyperspectral images (HSIs). It combines the spectral feature learning and spatial …