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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 211–220 of 286 papers

TitleStatusHype
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
Hyperspectral image classification using spectral-spatial LSTMs—0
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings—0
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network—0
Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network—0
Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field—0
Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis—0
HyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder—0
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification—0
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature EmbeddingCode0
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