In remote sensing image classification, distance measurements and classification criteria are equally important; and less accuracy of either would affect classification accuracy. Remote sensing image classification was performed by combining support vector machine (SVM) and [Formula: see text]-nearest neighbor (KNN). This was based on the separability of classes using SVM and the spatial and spectral characteristics of remote sensing data. Moreover, a distance formula is proposed as the measure criterion that considers both luminance and direction of the vectors. First, the SVM is trained and the support vectors (SVs) are obtained for each class. In the testing phase, newly tested samples were entered, and average distance between the test samples and SVs for each class are calculated using the distance formula. Finally, decisions are made to classify the test samples into the class with minimal average distance. This procedure is repeated until all test samples are classified. In the combinatorial algorithm, the nearest neighbor classifier is used to classify a testing sample, i.e. the average Euclidean distance between the testing data point to each set of SVs from different categories is calculated and the nearest neighbor classifier identifies the category with minimum distance. The proposed combinatorial algorithm has advantages over the conventional KNN for eliminating the [Formula: see text] parameter selection problem and reducing heavy learning time. A comparison is carried out with SVM, KNN and Spectral Angle Mapper (SAM) classification using ALOS/PALSAR and PSM images, and the effectiveness of the proposed algorithm is demonstrated.
Analysis and classification for remote sensing landscape based on remote sensing imagery is a popular research topic. In this paper, we propose a new remote sensing data classifier by incorporating the support vector machine (SVM) learning information into the K-nearest neighbor (KNN) classifier. The SVM is well known for its extraordinary generalization capability even with limited learning samples, and it is very useful for remote sensing applications as data samples are usually limited. The KNN has been widely used in data classification due to its simplicity and effectiveness. However, the KNN is instance-based and needs to keep all the training samples for classification, which could cause not only high computation complexity but also overfitting problems. Meanwhile, the performance of the KNN classifier is sensitive to the neighborhood size [Formula: see text] and how to select the value of the parameter [Formula: see text] relies heavily on practice and experience. Based on the observations that the SVM can contribute to the KNN on the problems of smaller training samples size as well as the selection of the parameter [Formula: see text], we propose a support vector nearest neighbor (abbreviated as SV-NN) hybrid classification approach which can simplify the parameter selection while maintaining classification accuracy. The proposed approach is consist of two stages. In the first stage, the SVM is performed on the training samples to obtain the reduced support vectors (SVs) for each of the sample categories. In the second stage, a nearest neighbor classifier (NNC) is used to classify a testing sample, i.e. the average Euclidean distance between the testing data point to each set of SVs from different categories is calculated and the NNC identifies the category with minimum distance. To evaluate the effectiveness of the proposed approach, firstly experiments of classification for samples from remote sensing data are evaluated, and then experiments of identifying different land covers regions in the remote sensing images are evaluated. Experimental results show that the SV-NN approach maintains good classification accuracy while reduces the training samples compared with the conventional SVM and KNN classification model.
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