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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 261–270 of 286 papers

TitleStatusHype
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning settingCode0
Does Normalization Methods Play a Role for Hyperspectral Image Classification?—0
Sparse Representation Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral Spatial Hyperspectral Image Classification—0
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification—0
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost TuningCode0
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural NetworkCode0
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural NetworkCode0
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image ClassificationCode0
Spatial Context based Angular Information Preserving Projection for Hyperspectral Image Classification—0
Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions—0
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