论文标题
高分辨率遥感图像从2021年Iflytek Challenge 2021耕种土地提取的获胜解决方案
The Winning Solution to the iFLYTEK Challenge 2021 Cultivated Land Extraction from High-Resolution Remote Sensing Image
论文作者
论文摘要
准确地从高分辨率远程图像中提取耕地是精确农业的基本任务。该报告介绍了我们的解决方案，以从高分辨率遥感图像中提取2021种培养的土地提取。挑战需要在非常高分辨率的多光谱遥感图像中细分耕地对象。我们建立了一个高效，有效的管道来解决此问题。我们首先将原始图像分为小图块，并在每个图块上分别执行实例分割。我们探索了几种实例分割算法，它们在自然图像上很好地工作，并开发了一组适用于遥感图像的有效方法。然后，我们将所有小图块的预测结果合并为无缝的连续分割结果，通过我们提出的重叠式融合策略。在挑战中，我们在486支球队中获得了第一名。
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.
