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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 181–190 of 286 papers

TitleStatusHype
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification—0
A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data—0
A 3-stage Spectral-spatial Method for Hyperspectral Image Classification—0
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification—0
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
A survey of active learning algorithms for supervised remote sensing image classification—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
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