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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 101–125 of 286 papers

TitleStatusHype
Hyperspectral Image Classification Based on Adaptive Sparse Deep Network—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions—0
Hyperspectral Images Classification Based on Multi-scale Residual Network—0
DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification—0
Feature Selection and Classification of Hyperspectral Images With Support Vector Machines—0
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning—0
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification—0
Forward-Forward Algorithm for Hyperspectral Image Classification: A Preliminary Study—0
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification—0
Does Normalization Methods Play a Role for Hyperspectral Image Classification?—0
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification—0
GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification—0
Hyperspectral Image Classification Based on Sparse Modeling of Spectral Blocks—0
Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification—0
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification—0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification—0
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification—0
Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification—0
Generating Hard Examples for Pixel-wise Classification—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
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