Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification
Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification
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DOI：
10.1109/tip.2018.2808767
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发表时间：
2018-06-01
影响因子：
10.6
通讯作者：
Paull, David
中科院分区：
文献类型：
--
作者：
Guo, Yiqing;Jia, Xiuping;Paull, David
遥感图像的爆炸性可用性已经挑战了监督分类算法，如支持向量机（SVM），因为训练样本往往是非常有限的，由于昂贵和费力的地面真实任务。多时相图像之间的时间相关性和光谱相似性为缓解这一问题提供了机会。提出了一种基于SVM的序列分类器训练（SCT-SVM）方法用于多时相遥感图像分类。该方法利用先前图像的分类器来减少输入图像的分类器训练所需的训练样本的数量。对于每个输入图像，首先基于一组先前分类器的时间趋势预测粗略分类器。然后，预测的分类器被微调到具有当前训练样本的更准确的位置。这种方法可以逐步应用于连续的图像数据，从每个图像中只需要少量的训练样本。Sentinel-2A多时相数据在澳大利亚的一个农业区进行了实验。实验结果表明，与现有的两种模型传递算法相比，SCT-SVM具有更好的分类精度。当训练数据不足时，与没有先前图像的辅助相比，所提出的SCT-SVM对输入图像的总体分类准确率从76.18%提高到94.02%。这些结果表明，利用先验信息从以前的图像可以提供有利的援助，后来的图像在多时相图像分类。
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 paper, 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 first 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.