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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 81–90 of 286 papers

TitleStatusHype
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction—0
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model—0
Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification—0
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification—0
MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image ClassificationCode0
When Segmentation Meets Hyperspectral Image: New Paradigm for Hyperspectral Image ClassificationCode0
DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature Extraction for Hyperspectral Image Classification—0
Correlation-Based Band Selection for Hyperspectral Image ClassificationCode0
Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification—0
DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image ClassificationCode0
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