computer-vision-1

Classification Of Hyperspectral Images

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image

Yaoming Cai, Xiaobo Liu, Z. Cai•Tue Apr 16 2019

This article proposes a unified BS framework, BS Network (BS-Net), which consists of a band attention module (BAM), which aims to explicitly model the nonlinear interdependences between spectral bands, and a reconstruction network (RecNet) which is used to restore the original HSI from the learned informative bands, resulting in a flexible architecture.

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Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images

R. Chellappa, Ilya Kavalerov, Weilin Li, W. Czaja•Sun Jun 16 2019

Results indicate that the Fourier scattering transform is highly effective at representing spectral data when compared with other state-of-the-art spectral-spatial classification methods.

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Fast forward feature selection for the nonlinear classification of hyperspectral images

M. Fauvel, Clément Dechesne, A. Zullo, F. Ferraty•Sun Jan 04 2015

A fast forward feature selection algorithm based on a Gaussian mixture model (GMM) classifier that performs very well in terms of classification accuracy and processing time and contains very few spectral channels.

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Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification

Shengjie Kris Liu, Haowen Luo, Ying-Ching Tu, Zhi He, Jun Yu Li•Sat Jun 30 2018

In this paper, we propose a wide contextual residual network (WCRN) with active learning (AL) for remote sensing image (RSI) classification. Although ResNets have achieved great success in various applications (e.g. RSI classification), its performance is limited by the requirement of abundant labeled samples. As it is very difficult and expensive to obtain class labels in real world, we integrate the proposed WCRN with AL to improve its generalization by using the most informative training samples. Specifically, we first design a wide contextual residual network for RSI classification. We then integrate it with AL to achieve good machine generalization with limited number of training sampling. Experimental results on the University of Pavia and Flevoland datasets demonstrate that the proposed WCRN with AL can significantly reduce the needs of samples.

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Classification of Hyperspectral Images Using SVM with Shape-Adaptive Reconstruction and Smoothed Total Variation

Kangning Cui, R. Li, R. Plemmons, R. Chan•Mon Mar 28 2022

Experiments show that SaR-SVM- STY outperforms the SVM-STV method with a few training labels, demonstrating the significance of reconstructing hy-perspectral images before classification.

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