کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
11012386 1800229 2018 21 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification
ترجمه فارسی عنوان
یک چارچوب نسبی نیمه نظارتی با ویژگی های یادگیری عمیق برای طبقه بندی تصویر تصویر تصویربرداری با دقت بالا
کلمات کلیدی
طبقه بندی صحنه، یادگیری عمیق، خود برچسب تصاویر سنجش از دور با وضوح بالا،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی
High resolution remote sensing (HRRS) image scene classification plays a crucial role in a wide range of applications and has been receiving significant attention. Recently, remarkable efforts have been made to develop a variety of approaches for HRRS scene classification, wherein deep-learning-based methods have achieved considerable performance in comparison with state-of-the-art methods. However, the deep-learning-based methods have faced a severe limitation that a great number of manually-annotated HRRS samples are needed to obtain a reliable model. However, there are still not sufficient annotation datasets in the field of remote sensing. In addition, it is a challenge to get a large scale HRRS image dataset due to the abundant diversities and variations in HRRS images. In order to address the problem, we propose a semi-supervised generative framework (SSGF), which combines the deep learning features, a self-label technique, and a discriminative evaluation method to complete the task of scene classification and annotating datasets. On this basis, we further develop an extended algorithm (SSGA-E) and evaluate it by exclusive experiments. The experimental results show that the SSGA-E outperforms most of the fully-supervised methods and semi-supervised methods. It has achieved the third best accuracy on the UCM dataset, the second best accuracy on the WHU-RS, the NWPU-RESISC45, and the AID datasets. The impressive results demonstrate that the proposed SSGF and the extended method is effective to solve the problem of lacking an annotated HRRS dataset, which can learn valuable information from unlabeled samples to improve classification ability and obtain a reliable annotation dataset for supervised learning.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 145, Part A, November 2018, Pages 23-43
نویسندگان
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