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.