Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing 100876, China.
Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Beijing 100125, China.
Sensors (Basel). 2023 Mar 31;23(7):3643. doi: 10.3390/s23073643.
It is very significant for rural planning to accurately count the number and area of rural homesteads by means of automation. The development of deep learning makes it possible to achieve this goal. At present, many effective works have been conducted to extract building objects from VHR images using semantic segmentation technology, but they do not extract instance objects and do not work for densely distributed and overlapping rural homesteads. Most of the existing mainstream instance segmentation frameworks are based on the top-down structure. The model is complex and requires a large number of manually set thresholds. In order to solve the above difficult problems, we designed a simple query-based instance segmentation framework, QueryFormer, which includes an encoder and a decoder. A multi-scale deformable attention mechanism is incorporated into the encoder, resulting in significant computational savings, while also achieving effective results. In the decoder, we designed multiple groups, and used a Many-to-One label assignment method to make the image feature region be queried faster. Experiments show that our method achieves better performance (52.8AP) than the other most advanced models (+0.8AP) in the task of extracting rural homesteads in dense regions. This study shows that query-based instance segmentation framework has strong application potential in remote sensing images.
通过自动化手段准确统计农村宅基地的数量和面积对农村规划具有重要意义。深度学习的发展使得这一目标成为可能。目前，已经有许多有效的工作利用语义分割技术从高分辨率图像中提取建筑物对象，但这些工作无法提取实例对象，也无法处理密集分布和重叠的农村宅基地。现有的大多数主流实例分割框架都是基于自上而下的结构。这些模型结构复杂，需要大量手动设置的阈值。为了解决上述难题，我们设计了一种简单的基于查询的实例分割框架 QueryFormer，它包括一个编码器和解码器。在编码器中加入了多尺度可变形注意力机制，从而显著节省了计算量，同时也取得了有效的结果。在解码器中，我们设计了多组，并使用多对一标签分配方法，使图像特征区域能够更快地被查询到。实验表明，我们的方法在密集区域提取农村宅基地的任务中表现优于其他最先进的模型（+0.8AP），达到了 52.8AP 的性能。本研究表明，基于查询的实例分割框架在遥感图像中有很强的应用潜力。
