超像素与主动学习相结合的遥感影像变化检测方法

针对高分辨率遥感影像变化检测结果较破碎,易产生椒盐噪声、监督训练过程中人工标注成本较高、训练样本冗余以及大量未标注样本信息未有效利用等问题,提出一种超像素与主动学习相结合的高分辨率遥感影像变化检测方法。利用超像素分割算法得到超像素对象,提取其光谱和纹理特征;引入并借助主动学习样本选择策略充分利用未标注样本信息,挖掘不确定性最大、最易错分的样本交由用户人工标注;为保证所选样本的多样性,加入基于余弦角距离的样本相似性度量,以减少样本间信息冗余,在减轻人工标注负担的同时获得良好的分类性能。通过对2组不同场景的遥感影像的实验,表明本文提出的2种方法能够在标注少量训练样本的情况下获得较好的变化检测结果,且加入样本相似性度量的变化检测方法在有效减少人工标注成本和训练样本冗余的同时,能够更快地达到收敛、提升检测质量。

Change Detection Approach for High Resolution Remotely Sensed Images Based on Superpixel and Active Learning

In terms of change detection with high resolution remote sensing images, there are still some unresolved problems such as scattered plots with ragged boundaries in output, being prone to occurrence of “salt-and-pepper” noise, expensive cost of manual annotation in the process of supervised training, redundancy of training samples, underutilization of information in unlabeled samples and so on. In order to address these problems, this paper proposes a new high resolution remote sensing image change detection method by combining the superpixel segmentation technology and Active Learning (AL) approaches. The proposed method consists of the following steps. Firstly the difference image is derived from two temporal remote sensing images. Subsequently the lattice-like homogenous superpixel are obtained by applying the Simple Linear Iterative Clustering (SLIC) algorithm. Simultaneously, we compare the SLIC algorithm with entropy-rate-based and modified-watershed-based superpixel generating algorithms respectively by means of homogeneity of superpixel and their coherence with image object boundaries. Then we compute the means and standard deviations of three bands of superpixel objects as spectral features and extract the entropy, energy and angular second moment by employing Gray-Level Co-occurrence Matrix (GLCM) as texture features. After that, initial training samples are randomly selected and labeled by introducing and following the Margin Sampling (MS) active learning sample selection strategy which is a kind of SVM based AL algorithm taking advantage of SVM geometrical properties and suitable for bipartition problems. A cosine distance based sample similarity measurement called Angle Based Diversity (ABD) is introduced to relief redundancy and ensure diversity of the selected samples. Lastly change detection is carried out according to the extracted information from trained samples. The proposed algorithms (SLIC-MS, SLIC-MS+ABD) are utilized to process WorldViewⅡmultispectral remote sensing data of urban and suburb scenes and the detection result from proposed sampling is compared with that from random sampling to explain detection accuracy of our methods. To illustrate the efficiency of methods proposed in this article, we investigate the iterative times of three techniques for reaching the same detection accuracy. Experimental results confirm that both SLIC-MS and SLIC-MS+ABD can reduce manual labeling cost and achieve better change detection quality than random sampling methods. They also indicate that the two proposed methods can find out samples with high uncertainty, which can be labeled by user themselves, from the unlabeled sample pool by making full use of and mining unlabeled sample information. Compared with the other two methods, SLIC-MS+ABD is more accurate with respect to identical data sets (the same two mentioned remote sensing images) and the same labeled sample number because the diversity of new selected samples has been considered in the learning process. In addition, SLIC-MS+ABD can obviously reduce iterative times to converge for achieving the same detection accuracy than other two approaches. On the basis of the experiment, it can be concluded that our proposed methods greatly relief the amount of user marking and acquire good change detection performance on high resolution remote sensing data sets as well. Experimental results also indicate that the methods implemented in this article saliently exhibit their advantages of manual cost reduction in sample labeling, avoidance of training sample redundancy to reach the same change detection quality for the same data set.