基于深度学习的卫星影像耕地变化检测方法及系统应用

A Method and System Application for Satellite Image Cultivated Land Change Detection Based on Deep Learning

为坚守18亿亩耕地红线,国家连续出台多项政策法规,坚决落实最严格的耕地保护制度,如何快速发现耕地变化是落实耕地保护制度的关键步骤。基于AI的卫星遥感影像变化检测方法以多时相变化检测为主,目前在理论层面取得较多突破,然而在实际落地应用中还存在一些难点。以工程化落地为目标,基于高分辨率卫星影像及年度变更调查成果数据,提出一种简易高效的耕地变化检测方法。利用融合多尺度特征的耕地语义分割模型(RABD)快速提取耕地图斑,经后处理与前一期耕地矢量图斑进行网格化擦除处理,得到疑似耕地减少图斑。并基于该方法封装成软件,可快速检测疑似耕地减少图斑,缩短人工作业时间约70%,为耕地保护工作提供了智能化技术选择。

In order to stick to the red line of 1.8 billion mu(the Chinese version of acre)of cultivated land,China has introduced a series of policies and regulations to resolutely implement the strictest cultivated land protection system.How to quickly discover the change of cultivated land is a key step of the cultivated land protection system.The AI based change detection method for satellite remote sensing images is mainly based on multi temporal change detection.Currently,there have been many breakthroughs in theory,but there are still some difficulties in practical application.Hence,this paper proposes an easy-to-use method to discover the change of cultivated land based on the high-resolution satellite images and the annual change survey results:Firstly,a semantic segmentation model(RABD)fusing multi-scale features is used to quickly extract the cultivated land patches;After post-processing and grid erasing processing with the previous farmland vector pattern,a suspected farmland reduction pattern was obtained.And based on this method,it is packaged into software,then,the cultivated land patches after post-processing is grid-based erased with previous cultivated land vector patches to obtain the suspected cultivated land reduction patches.And based on this method,packaged into software,it can quickly detect the cultivated land patches occupied by construction,shorten the manual operation time by about 70%,and provide an intelligent technology choice for cultivated land protection.