CSD和CDD结合下的最优遥感特征指数集构建及其在湿地信息提取中的应用

依托中分辨率成像光谱仪完整的数据序列和丰富的光谱信息,遥感特征指数在湿地生态系统发展变化的状态、趋向和规律研究方面发挥着不可替代的优势.传统类间距离判别的遥感特征指数选取中常存在过分依赖数据统计特征、入选指数与目标地类间生态学意义不明确、分类模型普适性差等局限性.基于此,本研究以河北省白洋淀湿地自然保护区为例,提出类可...

Construction of Optimal Remote Sensing Feature Index Set based on CSD and CDD and Its Application in Wetland Information Extraction

Based on the complete data sequence and abundant spectral information of the moderate resolution imaging spectrometer, remote sensing feature indices play an irreplaceable role in studying the stage, trend, and law of wetland ecosystem development. There are some limitations in selection of remote sensing feature indices for the traditional inter-class distance discrimination, such as over-dependence on statistical characteristics of data, indefiniteness of the inclusion index and the ecological significance of the target area, and poor applicability of the classification model. Based on this, Baiyangdian Wetland Nature Reserve in Hebei Province is selected as the research area. The method of Class Separation Discrimination (CSD) and Class Distance Discrimination (CDD) is proposed to construct the optimal remote sensing feature index. In addition, QUEST algorithm and Markov distance discrimination method are used to construct the classification decision tree model for the Baiyangdian Wetland extraction, which overcomes the shortcomings in the selection of traditional inter-class distance index. The results show that, firstly, the overall classification accuracy of the remote sensing feature index selected by the method combining CSD and CDD reach 91.32% and the kappa coefficient is 0.88 for the wetland information extraction in the study area. Compared with the traditional Classification and Regression Tree (CART) method, the classification accuracy is improved by 1.67%. Secondly, the selected optimal remote sensing feature index has a clear ecological meaning for the type of wetland extracted. For example, the redoxing process of emergent water plants under alternate dry and wet conditions determines that iron oxide index (IO) can be successfully selected to further separate the mixed cultivated land and emergent water plants. Furthermore, according to the OLI image of the study area in 2017, the decision tree model based on the combination of CSD and CDD and the decision tree model based on CART algorithm are applied to the classification of the OLI image in the study area in 2019. The overall classification accuracy and kappa coefficient of the model based on the combination of CSD and CDD are 86.97% and 0.83, respectively. The model based on cart method cannot meet the classification requirements. The research results show that the model based on the combination of CSD and CDD has good applicability and stability over years. In a word, the combination of CSD and CDD can effectively avoid the limitations of traditional remote sensing feature indices, and improve the applicability of classification model. It is a beneficial attempt to combine remote sensing feature index selection algorithm with decision tree in wetland information extraction.