کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4972991 1451254 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning
ترجمه فارسی عنوان
طبقه بندی نیمه حفاظت شده برای تصویر هیپرکترال بر اساس برچسب های چند تصمیم و یادگیری ویژگی های عمیق
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی
Semisupervised learning is widely used in hyperspectral image classification to deal with the limited training samples, however, some more information of hyperspectral image should be further explored. In this paper, a novel semisupervised classification based on multi-decision labeling and deep feature learning is presented to exploit and utilize as much information as possible to realize the classification task. First, the proposed method takes two decisions to pre-label each unlabeled sample: local decision based on weighted neighborhood information is made by the surrounding samples, and global decision based on deep learning is performed by the most similar training samples. Then, some unlabeled ones with high confidence are selected to extent the training set. Finally, self decision, which depends on the self features exploited by deep learning, is employed on the updated training set to extract spectral-spatial features and produce classification map. Experimental results with real data indicate that it is an effective and promising semisupervised classification method for hyperspectral image.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 120, October 2016, Pages 99-107
نویسندگان
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