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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
Hyperspectral Image Classification Based on Adaptive Sparse Deep Network—0
Hyperspectral Images Classification Based on Multi-scale Residual Network—0
Hyperspectral Image Classification Based on Sparse Modeling of Spectral Blocks—0
Hyperspectral Image Classification Based on Faster Residual Multi-branch Spiking Neural Network—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion—0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
Hyperspectral image classification using spectral-spatial LSTMs—0
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach—0
Spectral Graph Reasoning Network for Hyperspectral Image Classification—0
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