کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
555950 1451269 2015 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination
ترجمه فارسی عنوان
یک رویکرد طبقه بندی تصویر نیمه نظارت نیمه نظارت بر اساس اطلاعات مکانی فضایی و ترکیبی طبقه بندی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی

In the process of semi-supervised hyperspectral image classification, spatial neighborhood information of training samples is widely applied to solve the small sample size problem. However, the neighborhood information of unlabeled samples is usually ignored. In this paper, we propose a new algorithm for hyperspectral image semi-supervised classification in which the spatial neighborhood information is combined with classifier to enhance the classification ability in determining the class label of the selected unlabeled samples. There are two key points in this algorithm: (1) it is considered that the correct label should appear in the spatial neighborhood of unlabeled samples; (2) the combination of classifier can obtains better results. Two classifiers multinomial logistic regression (MLR) and k-nearest neighbor (KNN) are combined together in the above way to further improve the performance. The performance of the proposed approach was assessed with two real hyperspectral data sets, and the obtained results indicate that the proposed approach is effective for hyperspectral classification.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 105, July 2015, Pages 19–29
نویسندگان
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