Classification of high resolution satellite images using improved U-Net

• Autorzy: Wang Yong , Zhang Dongfang , Dai Guangming

Identyfikatory

DOI

10.34768/amcs-2020-0030

• Języki publikacji: EN

Abstrakty

EN

Satellite image classification is essential for many socio-economic and environmental applications of geographic information systems, including urban and regional planning, conservation and management of natural resources, etc. In this paper, we propose a deep learning architecture to perform the pixel-level understanding of high spatial resolution satellite images and apply it to image classification tasks. Specifically, we augment the spatial pyramid pooling module with image-level features encoding the global context, and integrate it into the U-Net structure. The proposed model solves the problem consisting in the fact that U-Net tends to lose object boundaries after multiple pooling operations. In our experiments, two public datasets are used to assess the performance of the proposed model. Comparison with the results from the published algorithms demonstrates the effectiveness of our approach.

Słowa kluczowe

EN

satellite image classification; deep learning; U-net; spatial pyramid pooling

PL

zdjęcia satelitarne; uczenie głębokie; U-net

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2020
• Tom: Vol. 30, no. 3
• Strony: 399--413
• Opis fizyczny: Bibliogr. 53 poz., rys., tab.

Twórcy

autor

Wang Yong

• School of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China

autor

Zhang Dongfang

• School of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China

autor

Dai Guangming

• School of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).