PCA-based feature reduction for hyperspectral remote sensing image classification
The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential
information of land objects through contiguous narrow spectral wavelength bands. The …
information of land objects through contiguous narrow spectral wavelength bands. The …
Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification
Hyperspectral image (HSI) usually holds information of land cover classes as a set of many
contiguous narrow spectral wavelength bands. For its efficient thematic mapping or …
contiguous narrow spectral wavelength bands. For its efficient thematic mapping or …
3D residual spatial–spectral convolution network for hyperspectral remote sensing image classification
Hyperspectral remote sensing images (HRSI) are 3D image cubes that contain hundreds of
spectral bands and have two spatial dimensions and one spectral dimension. HRSI analysis …
spectral bands and have two spatial dimensions and one spectral dimension. HRSI analysis …
Hyperspectral band selection using attention-based convolutional neural networks
Hyperspectral imaging has become a mature technology which brings exciting possibilities
in various domains, including satellite image analysis. However, the high dimensionality and …
in various domains, including satellite image analysis. However, the high dimensionality and …
Dimensionality reduction and classification of hyperspectral remote sensing image feature extraction
H Li, J Cui, X Zhang, Y Han, L Cao - Remote Sensing, 2022 - mdpi.com
Terrain classification is an important research direction in the field of remote sensing.
Hyperspectral remote sensing image data contain a large amount of rich ground object …
Hyperspectral remote sensing image data contain a large amount of rich ground object …
Effective feature extraction through segmentation-based folded-PCA for hyperspectral image classification
The remote sensing hyperspectral images (HSIs) usually comprise many important
information of the land covers capturing through a set of hundreds of narrow and contiguous …
information of the land covers capturing through a set of hundreds of narrow and contiguous …
Mutual information-driven feature reduction for hyperspectral image classification
A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral
wavelength bands, is a valuable source of data for ground cover examinations …
wavelength bands, is a valuable source of data for ground cover examinations …
NDVI based change detection in Sundarban Mangrove Forest using remote sensing data
As a global ecosystem, Sundarban mangrove forest plays a significant role by tackling
enormous CO 2, and other environmental impurities from air and water. It also protects …
enormous CO 2, and other environmental impurities from air and water. It also protects …
Segmentation-based linear discriminant analysis with information theoretic feature selection for hyperspectral image classification
The use of hyperspectral imaging sensors has greatly improved the classification of remotely
sensed data because of the abundant spectral information they offer. However, the …
sensed data because of the abundant spectral information they offer. However, the …
Land Cover Classification of Remote Sensing Images Based on Hierarchical Convolutional Recurrent Neural Network
X Fan, L Chen, X Xu, C Yan, J Fan, X Li - Forests, 2023 - mdpi.com
Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have gained
improved results in remote sensing image data classification. Multispectral image …
improved results in remote sensing image data classification. Multispectral image …