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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 221–230 of 286 papers

TitleStatusHype
A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion—0
Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification—0
An Ultralightweight Hybrid CNN Based on Redundancy Removal for Hyperspectral Image Classification—0
A novel statistical metric learning for hyperspectral image classification—0
Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer—0
Segmentation-Aware Hyperspectral Image Classification—0
Selective Transformer for Hyperspectral Image Classification—0
Self Supervised Learning for Few Shot Hyperspectral Image Classification—0
A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification—0
A fast and Accurate Similarity-constrained Subspace Clustering Framework for Unsupervised Hyperspectral Image Classification—0
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