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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 61–70 of 286 papers

TitleStatusHype
HyperKAN: Kolmogorov-Arnold Networks make Hyperspectral Image Classificators SmarterCode1
Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation FrameworkCode1
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
Efficient Deep Learning of Non-local Features for Hyperspectral Image ClassificationCode1
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image ClassificationCode1
DCN-T: Dual Context Network with Transformer for Hyperspectral Image ClassificationCode1
Correlation-Based Band Selection for Hyperspectral Image ClassificationCode0
Convolution Based Spectral Partitioning Architecture for Hyperspectral Image ClassificationCode0
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image ClassificationCode0
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral ImageCode0
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