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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
Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification—0
Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification—0
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification—0
SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification—0
Sparse Deformable Mamba for Hyperspectral Image Classification—0
Sparse Representation Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral Spatial Hyperspectral Image Classification—0
Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification—0
Spatial-Aware Dictionary Learning for Hyperspectral Image Classification—0
Spatial Context based Angular Information Preserving Projection for Hyperspectral Image Classification—0
Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification—0
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