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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 91–100 of 286 papers

TitleStatusHype
Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis—0
Discrete Cosine Transform-Based Joint Spectral-Spatial Information Compression and Band Correlation Calculation for Hyperspectral Feature ExtractionCode0
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification—0
Selective Transformer for Hyperspectral Image Classification—0
Hyperspectral Image Classification Based on Faster Residual Multi-branch Spiking Neural Network—0
Multi-head Spatial-Spectral Mamba for Hyperspectral Image ClassificationCode0
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification—0
Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification—0
Spectral Graph Reasoning Network for Hyperspectral Image Classification—0
Boosting Hyperspectral Image Classification with Gate-Shift-Fuse Mechanisms in a Novel CNN-Transformer Approach—0
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