A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning
| Autor: | Baipeng Li, Dailiang Peng, Xuan Yang, Zhengchao Chen, Pan Chen, Bing Zhang |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Contextual image classification Computer science business.industry Deep learning Computer Vision and Pattern Recognition (cs.CV) Big data Computer Science - Computer Vision and Pattern Recognition Machine learning computer.software_genre Convolutional neural network ComputingMilieux_GENERAL Transformation (function) Artificial intelligence Enhanced Data Rates for GSM Evolution business Scale (map) Image resolution computer |
| Zdroj: | IGARSS |
| DOI: | 10.48550/arxiv.1908.03438 |
| Popis: | The land-use map is an important data that can reflect the use and transformation of human land, and can provide valuable reference for land-use planning. For the traditional image classification method, producing a high spatial resolution (HSR), land-use map in large-scale is a big project that requires a lot of human labor, time, and financial expenditure. The rise of the deep learning technique provides a new solution to the problems above. This paper proposes a fast and precise method that can achieve large-scale land-use classification based on deep convolutional neural network (DCNN). In this paper, we optimize the data tiling method and the structure of DCNN for the multi-channel data and the splicing edge effect, which are unique to remote sensing deep learning, and improve the accuracy of land-use classification. We apply our improved methods in the Guangdong Province of China using GF-1 images, and achieve the land-use classification accuracy of 81.52%. It takes only 13 hours to complete the work, which will take several months for human labor. Comment: Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2019 |
| Databáze: | OpenAIRE |
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