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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 211–220 of 286 papers

TitleStatusHype
Hyperspectral Image Classification Based on Adaptive Sparse Deep Network—0
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network—0
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers—0
A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification—0
Sparse Bayesian approach for metric learning in latent spaceCode0
Multiscale Principle of Relevant Information for Hyperspectral Image ClassificationCode0
Convolution Based Spectral Partitioning Architecture for Hyperspectral Image ClassificationCode0
Optimizing CNN-based Hyperspectral ImageClassification on FPGAsCode0
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral ImagesCode0
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