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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 171–180 of 286 papers

TitleStatusHype
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification—0
Randomized Principal Component Analysis for Hyperspectral Image Classification—0
Robust hyperspectral image classification with rejection fields—0
Row-Sparse Discriminative Deep Dictionary 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
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks—0
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification—0
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