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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 141–150 of 286 papers

TitleStatusHype
Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification—0
Label Consistent Transform Learning for Hyperspectral Image Classification—0
Ladder Networks for Semi-Supervised Hyperspectral Image Classification—0
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification—0
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model—0
Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network—0
LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification—0
LiteDepthwiseNet: An Extreme Lightweight Network for Hyperspectral Image Classification—0
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification—0
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification—0
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