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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
Spatial-Spectral Hyperspectral Classification based on Learnable 3D Group ConvolutionCode0
3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image ClassificationCode0
Classification of Hyperspectral Images Using SVM with Shape-adaptive Reconstruction and Smoothed Total VariationCode0
BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral ImageCode0
Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image ClassificationCode0
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image ClassificationCode0
Deep supervised learning for hyperspectral data classification through convolutional neural networksCode0
Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image ClassificationCode0
Wide Contextual Residual Network with Active Learning for Remote Sensing Image ClassificationCode0
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image ClassificationCode0
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