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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 276–286 of 286 papers

TitleStatusHype
DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature Extraction for Hyperspectral Image Classification—0
Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification—0
Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification—0
Spatial--spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images—0
High Performance Hyperspectral Image Classification using Graphics Processing Units—0
Cross-domain CNN for Hyperspectral Image Classification—0
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers—0
Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification—0
Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification—0
Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
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