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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
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image ClassificationCode1
Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning—0
TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification—0
Triplet-Watershed for Hyperspectral Image ClassificationCode1
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
A Supervised Segmentation Network for Hyperspectral Image Classification—0
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image ClassificationCode1
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNNCode0
Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future ProspectsCode1
Attention-Based Second-Order Pooling Network for Hyperspectral Image ClassificationCode1
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification—0
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image ClassificationCode1
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks—0
Multi-direction Networks with Attentional Spectral Prior for Hyperspectral Image ClassificationCode1
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image ClassificationCode1
Hyperspectral classification of blood-like substances using machine learning methods combined with genetic algorithms in transductive and inductive scenarios—0
Fusion of Dual Spatial Information for Hyperspectral Image ClassificationCode1
LiteDepthwiseNet: An Extreme Lightweight Network for Hyperspectral Image Classification—0
SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification—0
Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping—0
Compressive spectral image classification using 3D coded convolutional neural network—0
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image ClassificationCode1
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification—0
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