A Hybrid Classification Approach Based on Support Vector Machine and K-Nearest Neighbor for Remote Sensing Data

Abstract

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.

Comparative Analysis of Machine Learning Algorithms in Automatic Identification and Extraction of Water Boundaries

“…(2) Machine learning methods: These methods feature pixel-based pattern recognition analysis, mainly including supervised and unsupervised classification techniques. The supervised methods mainly include neural network [21][22][23][24][25], support vector machine (SVM) [26][27][28], logistic regression [29,30], and random forest [31][32][33], and the unsupervised classification methods mainly include K-means clustering [34] and ISODATA clustering [35,36] methods. The machine learning algorithm has been widely used in remote sensing water extraction due to its high accuracy.…”

Classification for Remote Sensing Data With Improved CNN-SVM Method

“…At present, it has been widely used in image classification, face and speech recognition, handwritting font and traffic signal recognition, natural language processing, etc. [17], [18]. Support vector machine (SVM) has obvious advantages in solving small sample, nonlinear and high-dimensional pattern recognition, and avoids dimension disaster to some exten [19]- [21].…”

An Object-Based River Extraction Method via Optimized Transductive Support Vector Machine for Multi-Spectral Remote-Sensing Images

“…In addition, the particle swarm optimization algorithm with a mutation operator (AMPSO, Adaptive Mutation Particle Swarm Optimization) is presented to avoid the premature convergence phenomenon and lift up the iteration efficiency during the training process of the SVM classifier. In the experiments, the proposed method is compared with the PCA method [35], the KNN method [36], an SVM-based method and a PSO-SVM-based method.…”