基于FOA-SVM方法的长江中游悬浮物浓度遥感反演研究

遥感反演是监测水体表层悬浮物浓度分布的有效手段之一。然而,常用的经验回归模型是建立在大样本的理论假设之上的,而大多数情况下所获取的样本数实际上是十分有限的,因而有必要引入基于小样本的新的反演模型。支持向量机(SVM)建立在结构风险最小原理和VC维理论基础上,其泛化能力强,适用于小样本回归模型。使用HJ1B卫星CCD2遥感影像结合长江中游实地同步采样数据建立悬浮物浓度SVM遥感反演模型,并采用果蝇优化算法(FOA)对模型参数进行了优化。结果表明,与传统经验回归模型相比,SVM模型具有较高的预报精度和稳定性;在SVM模型的参数优化中,FOA算法效果理想,其计算量也远小于网格搜索算法。最后,使用所建立的SVM模型对长江中游城陵矶附近长江和洞庭湖水体悬浮物浓度进行了反演,并对其空间分布特征进行了分析。结果显示,长江干流的悬浮泥沙浓度总体上明显小于洞庭湖,这主要是三峡工程下泄泥沙大幅减少造成的;洞庭湖浑浊的湖水汇入长江后,在城陵矶下游形成明显的混合带;而洞庭湖湖口悬浮物浓度明显高于其他湖区,这可能是该区域采砂活动的强烈扰动引起的。

A STUDY ON REMOTE SENSING RETRIEVAL OF SUSPENDED SEDIMENT CONCENTRATION IN MIDDLE YANGTZE RIVER BASED ON A FOA-SVM METHOD

Remote sensing inversion is one of the effective ways to monitor the distribution of suspended sediment concentration (SSC) in water surface. Nevertheless，the present empirical regression models are based on assumption of large samples, while the samples number are actually small in most cases, so it is necessary to introduce new retrieval models which could be based on small sample number. Support Vector Machine (SVM) is established on the theories of Structural Risk Minimization Principle and VC Dimension, it has better generalization ability and is suitable for small-sample regression model. In this paper, one SVM remote sensing retrieval model for SSC has been established with HJ1B CCD2 satellite images and synchronous in situ SSC data in the middle reaches of Yangtze River, and its parameters was optimized by using Fruitfly Optimization Algorithm (FOA). The results showed that, SVM model has higher prediction accuracy and stability than the traditional empirical regression models, and in the parameter optimization of SVM model, FOA algorithm has satisfactory results and much smaller calculation amount than Grid Search Algorithm. Finally, the already trained SVM model was used to retrieve SSC in the water of Yangtze River and Dongting Lake around Chenglingji, and the spatial distribution features of SSC was analyzed. The results showed that, SSC is significantly smaller in the mainstream of Yangtze River than in Dongting Lake. This is mainly caused by significant decrease in sediment discharge because of Three Gorges Project. As a result, one blending zone can be distinguished in the Yangtze River between Chenglingji and Honghu city when the muddy waters of Dongting Lake flows into the river. In addition, the SSC is significantly higher in the outlet channel than in the other area in Dongting Lake, which may be caused by the strong disturbance of the sand mining activity in the region.