کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6865035 | 1439554 | 2018 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A band selection method for airborne hyperspectral image based on chaotic binary coded gravitational search algorithm
ترجمه فارسی عنوان
یک روش انتخاب باند برای تصویر هیپرپرترورافی هوابرد مبتنی بر الگوریتم جستجو گرانشی کدگذاری باینری هرج و مرج
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Band selection is one of the most important topics in hyperspectral image classification for irrelevant band information and the high correlation between the adjacent bands. The main concern is to obtain the compact and effective bands to classify the image with the least impact for the classification accuracy. In general, band selection could be seen as a combinatorial optimization problem through defining an objective function based on the number of bands and classification accuracy. Therefore, in the paper, a novel band selection method based on a chaotic binary coded gravitational search algorithm (CBGSA) is proposed to reduce the dimensionality of airborne hyperspectral images. The proposed method is also compared with that of genetic algorithm (GA), binary coded particle swarm optimization (BPSO) algorithm, binary coded differential evolution (BDE) algorithm and binary coded cuckoo search (BCS) algorithm on some airborne hyperspectral images; furthermore, it is also compared with some other existing techniques such as Relief-F algorithm, minimum Redundancy Maximum Relevance (mRMR) criterion, and the optimum index (OI) criterion for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and might be applied for practical work of airborne hyperspectral image classification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 273, 17 January 2018, Pages 57-67
Journal: Neurocomputing - Volume 273, 17 January 2018, Pages 57-67
نویسندگان
Mingwei Wang, Youchuan Wan, Zhiwei Ye, Xianjun Gao, Xudong Lai,