The classification of remote sensing data plays an important role in all stages of remote sensing data processing and analysis. With the increase in the volume of remote sensing data, new problems concerning remote sensing big data classification tasks arise. Currently, the commonly used classifiers are usually designed for simple tasks to provide satisfactory results. However, for the processing of large volumes of remote sensing data, the scalability of classification efficiency and precision should be further investigated. Therefore, this study emphasizes on the comparisons of the scalability of typical remote sensing data classification methods to achieve this goal. Method: This study takes remote sensing image scene classification as an example and selects four well-known classification methods for comparison, namely, K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Sparse Representation-based Classifier (SRC), to conduct scalability analysis. The comparisons are conducted in terms of parameter sensitivity, effect of training sample data volume on classifier performance, effect of testing sample data volume on classifier performance, and effect of feature dimension on classifier performance. Results: The experimental results are as below: (1) The classifiers of KNN, RF, and L0-SRC are less parameter-sensitive than the classifiers of RBF-SVM, Linear-SVM, and L1-SRC. (2) In cases where the samples to be classified are fixed, all the classifiers tend to increase with the increase in the number of training samples. The SRC-type classification methods have the highest accuracy, followed by the SVM-type classification methods, the RF, and the KNN classifiers. In terms of overall classification time, the results show that the methods can be ranked as below: L0-SRC > L1-SRC > RF > RBF-SVM/Linear-SVM > KNN/L0-SRC-Batch. (3) In cases where the training samples are fixed, the classification accuracies of all the classifiers are seldom affected by the number of samples to be classified, which may be due to the learning abilities of all the different classifiers. (4) The feature dimension affects the efficiency and accuracy of different classifiers, in which SRC and KNN can obtain satisfactory results without high feature dimensions. SVM is tolerant to high feature dimensions and has a good learning ability with such high feature dimensions. By contrast, RF is insensitive to the increase in feature dimensions, and higher feature dimensions do not contribute much to the improvement of classification performance. Under such circumstances, the RBFSVM exhibits the best performance, followed by the L1-SRC classifier, the Linear-SVM classifier, and the RF and L0-SRC/L0-SRC-Batch classifiers. In terms of overall classification time, the classifiers of L1-SRC and L0-SRC are the most time-consuming, whereas the other classifiers have relatively higher efficiency. Conclusion: Different classification methods have different advantages and disadvantages. In the tasks of classifying a large volume of remote sensing data, the selection of classifiers should be balanced and based on their characteristics and practical applications. Generally, a classifier that is less parameter-sensitive and less time-consuming during classification and obtains more accurate classification results is preferable. © 2016, Science Press. All right reserved.
