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. Compared with those obtained without the assistance from previous images, the overall classification accuracy was improved from 76.2% to 93.8%, demonstrating that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

本文提出了一种基于SVM的序列分类器训练方法，通过利用多时相遥感数据之间的时间相关性和光谱相似性，减少了分类器训练所需标注数据量，并在澳大利亚的农业地区使用Sentinel-2A多时相数据进行了实验。结果表明，该方法比两种先进的模型转移算法实现了更好的分类精度，并且能够提高多时相图像分类的整体准确性。

基于SVM的遥感多时相图像分类的有效序列分类器训练