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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 111–120 of 286 papers

TitleStatusHype
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification—0
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction—0
Does Normalization Methods Play a Role for Hyperspectral Image Classification?—0
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification—0
GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification—0
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification—0
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification—0
Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification—0
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