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arXiv 2202.10974cs.CVcs.AIcs.LG

The Winning Solution to the iFLYTEK Challenge 2021 Cultivated Land Extraction from High-Resolution Remote Sensing Image

Beijing Forestry University(北京林业大学);Tsinghua University(清华大学);Chinese Institute for Brain Research (CIBR)(中国脑科学研究中心(CIBR))

Zhen Zhao, Yuqiu Liu, Gang Zhang, Liang Tang, Xiaolin Hu

英文摘要:

Extracting cultivated land accurately from high-resolution remote images is a basic task for precision agriculture. This report introduces our solution to the iFLYTEK challenge 2021 cultivated land extraction from high-resolution remote sensing image. The challenge requires segmenting cultivated land objects in very high-resolution multispectral remote sensing images. We established a highly effective and efficient pipeline to solve this problem. We first divided the original images into small tiles and separately performed instance segmentation on each tile. We explored several instance segmentation algorithms that work well on natural images and developed a set of effective methods that are applicable to remote sensing images. Then we merged the prediction results of all small tiles into seamless, continuous segmentation results through our proposed overlap-tile fusion strategy. We achieved the first place among 486 teams in the challenge.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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