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Your search keyword '"Jiao, Junnan"' showing total 17 results
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17 results on '"Jiao, Junnan"'

14. A comparison of multiple classifier combinations using different voting-weights for remote sensing image classification

Author

Qingjiu Tian, Kaijian Xu, Yinghao Lin, Huaifei Shen, and Jiao Junnan

Subjects
Contextual image classification, Computer science, media_common.quotation_subject, 0211 other engineering and technologies, 02 engineering and technology, Multiple classifier, Operational land imager, ComputingMethodologies_PATTERNRECOGNITION, Voting, 0202 electrical engineering, electronic engineering, information engineering, General Earth and Planetary Sciences, 020201 artificial intelligence & image processing, Classifier (UML), 021101 geological & geomatics engineering, media_common, Remote sensing
Abstract

Remote sensing image classification is a common application of remote sensing images. In order to improve the performance of Remote sensing image classification, multiple classifier combinations are used to classify the Landsat-8 Operational Land Imager (Landsat-8 OLI) images. Some techniques and classifier combination algorithms are investigated. The classifier ensemble consisting of five member classifiers is constructed. The results of every member classifier are evaluated. The voting strategy is experimented to combine the classification results of the member classifier. The results show that all the classifiers have different performances and the multiple classifier combination provides better performance than a single classifier, and achieves higher overall accuracy of classification. The experiment shows that the multiple classifier combination using producer’s accuracy as voting-weight (MCCmod2 and MCCmod3) present higher classification accuracy than the algorithm using overall accuracy as...

Published
2018

17. Sea surface Fresnel reflections difference driven de-glint algorithm for airborne optical images.

Author

Wang M, Wang L, Jiao J, Song Q, Ma C, Yang S, Ju W, Tian L, and Lu Y

Abstract

This paper presents a glint correction algorithm for high spatial resolution optical remote sensing imagery captured by the ER-2 Airborne Visual Infrared Imaging Spectrometer (AVIRIS). The algorithm employs linear and differential techniques to mitigate sun glint and sky glint effects, encompassing statistical glint reflections resulting from variations in imaging angles within strips and inter-strip variations due to Fresnel reflectance disparities. It aims to diminish Fresnel reflectance diversity on water surfaces and mitigate the distortions induced by glint reflectance during spectral and ocean color inversion. A comparative analysis of spectral and ocean color information in AVIRIS images before and after correction reveals enhanced accuracy following the glint correction. By systematically addressing multiple glint reflections and their ramifications, this method offers a valuable framework for correcting water surface glint in diverse high spatial resolution optical imagery.

Published
2024
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