Classification of High Resolution Urban Remote Sensing Images Using Deep Networks by Integration of Social Media Photos
| Autor: | Mingmin Chi, Yiqing Qin, Yijian Zeng, Xuan Liu, Zhiming Zhao, Yunfeng Zhang |
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| Přispěvatelé: | Multiscale Networked Systems (IvI, FNWI), IVI (FNWI), Department of Water Resources, UT-I-ITC-WCC, Faculty of Geo-Information Science and Earth Observation |
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
22/3 OA procedure Computer science 02 engineering and technology Image segmentation 010501 environmental sciences 01 natural sciences Convolutional neural network Support vector machine Urban planning Deep neural networks 0202 electrical engineering electronic engineering information engineering High resolution Social media photos 020201 artificial intelligence & image processing Social media Urban remote sensing images Image resolution Classifier (UML) 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | IGARSS 22018 IEEE International Geoscience & Remote Sensing Symposium: proceedings : July 22-27, 2018, Valencia, Spain, 7243-7246 STARTPAGE=7243;ENDPAGE=7246;TITLE=22018 IEEE International Geoscience & Remote Sensing Symposium 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018-Proceedings, 7243-7246 STARTPAGE=7243;ENDPAGE=7246;TITLE=2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018-Proceedings |
| DOI: | 10.1109/igarss.2018.8518538 |
| Popis: | In recent decades, it is easy to obtain remote sensing images which have been successfully applied to various applications, such as urban planning, hazard monitoring, etc. In particular, high resolution (HR) remote sensing (RS) images can better monitor our living environment from a broader spatial perspective. However, raw remote sensing images provide no labeling information to train a classifier, which usually is exploited to generate remote sensing maps. Based on our previous work, in the paper, an automatic classification system is proposed to classify high resolution urban RS images using deep neural networks, in particular, convolutional neural networks and fully convolutional networks. The labeling information is assigned on the context of both social media photos and HR remote sensing images by significantly reducing the cost of manual labeling without the necessity of remote sensing experts. The experiments carried out on high resolution remote sensing images acquired in the city Frankfurt taken by the Jilin-1 satellites confirm the effectiveness of the proposed strategy compared to the state of the art. |
| Databáze: | OpenAIRE |
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