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LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK WITH REMOTE SENSING DATA AND DIGITAL SURFACE MODEL

التفاصيل البيبلوغرافية
العنوان: LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK WITH REMOTE SENSING DATA AND DIGITAL SURFACE MODEL
المؤلفون: B. Liu, S. Du, X. Zhang
المصدر: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-3-2020, Pp 39-43 (2020)
بيانات النشر: Copernicus Publications, 2020.
سنة النشر: 2020
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Applied optics. Photonics
مصطلحات موضوعية: Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
الوصف: Land cover map is widely used in urban planning, environmental monitoring and monitoring of the changing world. This paper proposes a framework with convolutional neural network (CNN), object-based voting and conditional random field (CRF) for land cover classification. Both very-high-resolution (VHR) remote sensing images and digital surface model (DSM) are inputs of this CNN model. To solve the “salt and pepper” effect caused by pixel-based classification, an object-based voting classification is performed. And to capture accurate boundary of ground objects, a CRF optimization using spectral information, DSM and deep features extracted through CNN is applied. Area one of Vaihingen datasets is used for experiment. The experimental results show that method proposed in this paper achieve an overall accuracy of 95.57%, which demonstrate the effectiveness of proposed method.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2194-9042
2194-9050
Relation: https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/39/2020/isprs-annals-V-3-2020-39-2020.pdf; https://doaj.org/toc/2194-9042; https://doaj.org/toc/2194-9050
DOI: 10.5194/isprs-annals-V-3-2020-39-2020
URL الوصول: https://doaj.org/article/c0b82df198d54f3ab0f9a1c8d14e4389
رقم الأكسشن: edsdoj.0b82df198d54f3ab0f9a1c8d14e4389
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21949042
21949050
DOI:10.5194/isprs-annals-V-3-2020-39-2020