Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …

Hyperspectral image (HSI) classification is one of the fundamental tasks in HSI analysis.
Recently, many approaches have been extensively studied to improve the classification …

Hyperspectral images generally contain hundreds of contiguous spectral bands, which can
precisely discriminate the various spectrally similar land cover classes. However, such high …

Hyperspectral images (HSIs) provide invaluable information in both spectral and spatial
domains for image classification tasks. In this paper, we use semantic representation as a …

To effectively learn heterogeneous features extracted from raw LiDAR point cloud data for
landcover classification, a multiple kernel sparse representation classification (MKSRC) …

Sparse representation (SR) method has the advantages of good category distinguishing
performance, noise robustness, and data adaptiveness. In this article, a multi-feature …

Active learning (AL) is one of the popular approaches that can mitigate some of the
drawbacks of supervised classification. Although sparse representation classifier (SRC) has …

Kernel joint sparse representation (KJSR) performs joint sparse representation in the feature
space and has shown good performance for the hyperspectral image (HSI) classification. In …

Many researches have been proved that the most important information of high-dimensional
data (eg hyperspectral images, HSI) lies in a low-dimensional subspace spanned by some …

Joint sparse representation (JSR) is a commonly used classifier that recognizes different
objects with core features extracted from images. However, the generalization ability is weak …