  The explosive availability of remote sensing images has challenged supervised
classification algorithms such as Support Vector Machines (SVM), as training
samples tend to be highly limited due to the expensive and laborious task of
ground truthing. The temporal correlation and spectral similarity between
multitemporal images have opened up an opportunity to alleviate this problem.
In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is
proposed for multitemporal remote sensing image classification. The approach
leverages the classifiers of previous images to reduce the required number of
training samples for the classifier training of an incoming image. For each
incoming image, a rough classifier is firstly predicted based on the temporal
trend of a set of previous classifiers. The predicted classifier is then
fine-tuned into a more accurate position with current training samples. This
approach can be applied progressively to sequential image data, with only a
small number of training samples being required from each image. Experiments
were conducted with Sentinel-2A multitemporal data over an agricultural area in
Australia. Results showed that the proposed SCT-SVM achieved better
classification accuracies compared with two state-of-the-art model transfer
algorithms. When training data are insufficient, the overall classification
accuracy of the incoming image was improved from 76.18% to 94.02% with the
proposed SCT-SVM, compared with those obtained without the assistance from
previous images. These results demonstrate that the leverage of a priori
information from previous images can provide advantageous assistance for later
images in multitemporal image classification.
