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Hyperspectral Image Classification

Hyperspectral Image Classification is a task in the field of remote sensing and computer vision. It involves the classification of pixels in hyperspectral images into different classes based on their spectral signature. Hyperspectral images contain information about the reflectance of objects in hundreds of narrow, contiguous wavelength bands, making them useful for a wide range of applications, including mineral mapping, vegetation analysis, and urban land-use mapping. The goal of this task is to accurately identify and classify different types of objects in the image, such as soil, vegetation, water, and buildings, based on their spectral properties.

( Image credit: Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification )

Papers

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Showing 181–190 of 286 papers

TitleStatusHype
Graph Convolutional Networks for Hyperspectral Image ClassificationCode1
Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network—0
Efficient Deep Learning of Non-local Features for Hyperspectral Image ClassificationCode1
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image ClassificationCode1
Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification—0
A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion—0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image ClassificationCode0
Hyperspectral Image Classification with Attention Aided CNNsCode1
Hyperspectral Image Classification Based on Sparse Modeling of Spectral Blocks—0
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