Overview of hyperspectral image classification

Jing-wen, 闫敬文 Yan; Hong-da, 陈宏达 Chen; Lei, 刘磊 Liu

doi:10.3788/ope.20192703.0680

Cited by 3 publications

(2 citation statements)

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“…In this paper, texture features, geographical features and exponential features are introduced into hyperspectral image classification, and four experimental schemes are constructed. CG method and PCA transform are used to classify the four experimental schemes, and SVM classifier is used to analyze and compare the classification results, and the following conclusions are obtained: (1) The classification accuracy of hyperspectral can be effectively improved by multi-feature fusion to a certain extent. (2) The accuracy obtained by CG method is higher than that obtained by PCA method.…”

Section: Discussionmentioning

confidence: 97%

“…Hyperspectral image classification is a technology that uses its rich spectral information to distinguish ground objects for each pixel in the image [1] . In recent years, some scholars have introduced "spatial information" into hyperspectral image classification to improve the classification accuracy through the dependence relationship between spatial pixels [2] .…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral image classification based on multi-feature fusion and different dimensionality reduction methods

Miao

Dou²,

Xu³

2023

Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022)

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The rich band information of hyperspectral images provides data support for accurate classification, but the information redundancy also brings bad effects to image classification. In order to improve the classification accuracy, this paper takes Liutang Town of Guilin City, Guangxi Province as the research area, and "OVS-1A/B" hyperspectral image as the data source. Based on two different dimensionality reduction methods, Principal Components Analysis (PCA) and CfsSubsetEval GreedyStepwise (CG), combined with Support Vector Machine (SVM), classifiers are used to classify hyperspectral images based on four multi-feature fusion schemes, and their classification accuracy is compared. The results show that: (1) In the two classification schemes with different dimensionality reduction methods, the classification accuracy of ground objects increases continuously with the increase of the types of fused features. (2) In the same combination scheme, the classification accuracy obtained by CG feature selection is higher than that obtained by PCA feature extraction. (3) The classification accuracy of scheme 4 using CG method for feature selection was the highest, the overall classification accuracy was 97.12%, and the Kappa coefficient was 0.9556.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%