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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 101–110 of 286 papers

TitleStatusHype
DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image ClassificationCode0
Hyperspectral Image Classification via Transformer-based Spectral-Spatial Attention Decoupling and Adaptive GatingCode0
Deep supervised learning for hyperspectral data classification through convolutional neural networksCode0
A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image ClassificationCode0
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost TuningCode0
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image ClassificationCode0
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image ClassificationCode0
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image ClassificationCode0
BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral ImageCode0
Deep Manifold Embedding for Hyperspectral Image ClassificationCode0
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