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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
MSHCNet: Multi-Stream Hybridized Convolutional Networks with Mixed Statistics in Euclidean/Non-Euclidean Spaces and Its Application to Hyperspectral Image Classification—0
Multi-branch fusion network for hyperspectral image classification—0
Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions—0
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification—0
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification—0
Multimodal Hyperspectral Image Classification via Interconnected Fusion—0
Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification—0
MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification—0
Multi-Scale U-Shape MLP for Hyperspectral Image Classification—0
Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection—0
Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification—0
Naive Gabor Networks for Hyperspectral Image Classification—0
Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification—0
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification—0
Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification—0
Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification—0
Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification—0
Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping—0
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification—0
Randomized based restricted kernel machine for hyperspectral image classification—0
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification—0
Randomized Principal Component Analysis for Hyperspectral Image Classification—0
Robust hyperspectral image classification with rejection fields—0
Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification—0
Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer—0
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