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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 1–10 of 286 papers

TitleStatusHype
MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone ArchitectureCode0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance—0
Hyperspectral Image Classification via Transformer-based Spectral-Spatial Attention Decoupling and Adaptive GatingCode0
Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification—0
Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image ClassificationCode1
HyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder—0
Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image ClassificationCode0
Dynamic Memory-enhanced Transformer for Hyperspectral Image Classification—0
Expert Kernel Generation Network Driven by Contextual Mapping for Hyperspectral Image ClassificationCode0
3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image ClassificationCode0
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