Autor:

Wang, Yong ; Zhang, Dongfang ; Dai, Guangming

Współtwórca:

Kołodziej, Joanna - ed. ; Pllana, Sabri - ed. ; Vitabile , Salvatore - ed.

Tytuł:

Classification of high resolution satellite images using improved U-Net

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, volume 30 (2020)

Temat i słowa kluczowe:

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

Abstract:

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2020

Typ zasobu:

artykuł

DOI:

10.34768/amcs-2020-0030

Strony:

399-413

Źródło:

AMCS, volume 30, number 3 (2020) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego