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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
SpectralFormer: Rethinking Hyperspectral Image Classification with TransformersCode1
Spectral-Spatial Global Graph Reasoning for Hyperspectral Image ClassificationCode1
High Performance Hyperspectral Image Classification using Graphics Processing Units—0
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
3D/2D regularized CNN feature hierarchy for Hyperspectral image classification—0
ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification—0
A survey of active learning algorithms for supervised remote sensing image classification—0
A fast and Accurate Similarity-constrained Subspace Clustering Framework for Unsupervised Hyperspectral Image Classification—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—0
Robust Self-Ensembling Network for Hyperspectral Image ClassificationCode1
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