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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 151–175 of 286 papers

TitleStatusHype
SpectralMamba: Efficient Mamba for Hyperspectral Image Classification—0
A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification—0
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings—0
Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification—0
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—0
Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network—0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance—0
Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field—0
Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis—0
HyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder—0
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification—0
Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary Sparse Representation—0
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification—0
Is Pretraining Necessary for Hyperspectral Image Classification?—0
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification—0
Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification—0
Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification—0
Label Consistent Transform Learning for Hyperspectral Image Classification—0
Ladder Networks for Semi-Supervised Hyperspectral Image Classification—0
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification—0
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model—0
Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network—0
Boosting Hyperspectral Image Classification with Gate-Shift-Fuse Mechanisms in a Novel CNN-Transformer Approach—0
LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification—0
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