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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 251–260 of 286 papers

TitleStatusHype
Deep Manifold Embedding for Hyperspectral Image ClassificationCode0
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost TuningCode0
3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image ClassificationCode0
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning settingCode0
Going Deeper with Contextual CNN for Hyperspectral Image ClassificationCode0
Deep Learning for Classification of Hyperspectral Data: A Comparative ReviewCode0
Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image ClassificationCode0
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural NetworkCode0
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image ClassificationCode0
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