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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 71–80 of 286 papers

TitleStatusHype
Compressive spectral image classification using 3D coded convolutional neural network—0
A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification—0
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—0
A fast and Accurate Similarity-constrained Subspace Clustering Framework for Unsupervised Hyperspectral Image Classification—0
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification—0
3D/2D regularized CNN feature hierarchy for Hyperspectral image classification—0
Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for Hyperspectral Image Classification—0
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