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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 241–250 of 286 papers

TitleStatusHype
Is Pretraining Necessary for Hyperspectral Image Classification?—0
Multi-branch fusion network for hyperspectral image classification—0
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification—0
Generating Hard Examples for Pixel-wise Classification—0
Ladder Networks for Semi-Supervised Hyperspectral Image Classification—0
Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image ClassificationCode0
Hyperspectral Image Classification in the Presence of Noisy LabelsCode0
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification—0
Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification—0
Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification—0
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