基于决策融合的SPOT-6影像土地覆被分类研究

多分类器决策融合方法在提高遥感影像分类的准确性和可靠性方面已表现出了巨大潜力,但这一过程中对所有像元多次分类会产生巨大的时间代价,为改善这一问题,本文提出了主分类器的概念.在青海湟水流域确定2个试验区,对7种常用的分类器进行评估,排除精度较低的3种分类器后,选择支持向量机(Support Vector Machine,...

Land Cover Classification for SPOT-6 Image from Decision Fusion Method

The decision fusion of multi-classifiers has shown great potential in improving the accuracy and reliability of remote sensing image classification. However, multi-classification of all pixels is usually time-consuming. In order to solve this problem, this paper puts forward the concept of master classifier based on existing studies. Firstly, two experimental areas were selected in the Huangshui river basin of Qinghai province: region A representing urban area with serious human activities and complex spectra, and region B representing rural and mountainous area with relatively simple spectra. To obtain a high classification accuracy, seven different commonly used classifiers were selected for decision fusion. Using the testing samples, the classifiers with low accuracy were excluded based on the average accuracy of two regions. Excluded classifies were Naive Bayesian (NB), K-NearestNeighbor (KNN), and Decision Tree (DT). The other four classifiers including Gradient Boosting Decision Tree (GBDT), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were kept to establish the decision rules for SPOT-6 images classification. Particularly, the GBDT with the highest accuracy among all classifiers was regarded as the master classifier. After the classification by GBDT, the pixels with low confidence were classified again using the other three classifiers, and the decision fusion was made together with the result of GBDT to select the best classification result. The results show that, 38.10% and 65.26% of the pixels in the two regions were classified by the master classifier alone, respectively, and the misclassification rate was 1.57% and 2.18%, respectively. For the regions of decision making using multiple classifiers, the overall classification accuracy was respectively 2.49% and 3.66% higher than that using GBDT. On the whole, the decision fusion improved the overall classification accuracy of the two regions by 1.18% and 1.09% respectively, effectively reduced the "salt and pepper " noise in the results, and achieved a more homogeneous classification accuracy. Compared with the existing decision fusion researches, the use of the master classifier not only ensures the accuracy of classification, but also improves the classification efficiency and maintains a good consistency of the classification results.