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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 231–240 of 286 papers

TitleStatusHype
BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral ImageCode0
Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral imagesCode0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
1D-Convolutional Capsule Network for Hyperspectral Image Classification—0
Superpixel Contracted Graph-Based Learning for Hyperspectral Image ClassificationCode0
Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field—0
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification—0
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image ClassificationCode0
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