由于各种背景因素(云、阴影等)的干扰及水体本身在光谱、形状上的复杂变化,遥感水体信息提取技术无法完全实现业务化自动运行。以复杂背景下多样水体的自动解译为目标,按实用化要求提出了新的水体提取流程,通过整合自适应分割与分类、局部迭代、矢量化后处理等关键技术,初步完成了基于Landsat TM/ETM影像的大规模水体信息自动提取。选取了两个较典型的研究区域:巴尔喀什湖地区和长江中下游地区,应用本文方法对两个地区各8景TM影像进行水体自动提取,发现可以有效克服薄云、阴影、水质变化等干扰因素,取得了较好的提取效果,能初步满足实际应用需求。

Suffering from the interference of all kinds of background factors (clouds, shadows and pollution) and complicated changes of water itself in spectrum and shape, the process of water extraction can still not be completely automatic. Based on the goal of automatic interpretation of diverse water bodies under complicated backgrounds a new water extraction process is put forward by practical requirements. Adaptive segmentation or classification, local iteration, and post processing are the key steps of the proposed method mainly designed for the automatic extraction of large-scale water body information from Landsat TM/ETM images. The experiments are conducted on two typical research areas, Balkhash Lake and the Yangtze River region, each with 8 scenes of TM images. And we found that the proposed method can effectively overcome the interference factors, such as the cloud, shadow, water changes, which can achieve better extraction effect and preliminarily satisfy the actual application needs.