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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
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
Dynamic Memory-enhanced Transformer for Hyperspectral Image Classification—0
Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction—0
DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification—0
ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for Hyperspectral Image Classification—0
Band Attention Convolutional Networks For Hyperspectral Image Classification—0
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification—0
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification—0
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