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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 241–250 of 286 papers

TitleStatusHype
TPPI-Net: Towards Efficient and Practical 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
DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification—0
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification—0
Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification—0
Dynamic Memory-enhanced Transformer for Hyperspectral Image Classification—0
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
Does Normalization Methods Play a Role for Hyperspectral Image Classification?—0
Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification—0
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