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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 151–160 of 286 papers

TitleStatusHype
Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification—0
Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification—0
ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for Hyperspectral Image Classification—0
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification—0
SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image ClassificationCode0
Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification—0
Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification—0
A 3D 2D convolutional Neural Network Model for Hyperspectral Image Classification—0
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification—0
PCA-domain Fused Singular Spectral Analysis for fast and Noise-Robust Spectral-Spatial Feature Mining in Hyperspectral ClassificationCode0
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