Interactive Change Detection Using High Resolution Remote Sensing Images Based on Active Learning with Gaussian Processes

Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem. Conventional supervised methods need lots of annotations to classify the land cover categories and detect their changes. Besides, the training set in supervised methods often has lots of redundant samples without any essential information. In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection techniques. In our method, there is no annotation of actual land cover category at the beginning. First, we find a certain number of the most representative objects in unsupervised way. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection results do not change notably. The artificial labelling can be reduced substantially, and a desirable detection result can be obtained in a few iterations. The experiments on Geo-Eye1 and WorldView2 remote sensing images demonstrate the effectiveness and efficiency of our proposed method. * Corresponding author: E-mail: [email protected] 1. ITRODUCTION The change detection technique for multi-temporal high resolution remote sensing images plays an important role in many applications, such as monitoring land cover transitions, nature disasters (e.g. earthquake, tsunami), land desertification and urbanization. There are many algorithms of change detection in literature which can be roughly divided into two categories: unsupervised and supervised. Among unsupervised methods, the land cover transitions are often detected from spectral reflectance properties of high resolution remote sensing images, and the difficulty lies in choosing an appropriate threshold. Among supervised methods, from the labelled training samples, we can use the classification results from different temporal remote sensing images to detect the land cover transitions. In general, unsupervised methods can provide binary maps which only present “change” or “no change” information. Supervised methods can provide the land cover transitions with knowing the land cover categories in each temporal additionally. The unsupervised techniques are usually affected by certain objective factors like the atmosphere conditions, sensor calibration, etc. On the other hand, the supervised techniques need lots of manual intervention and will increase the cumulative errors in the process of comparing classification results. From the previous experience, the user should choose an appropriate change detection algorithm for the specific requirement in practice. Another difficulty for classification and change detection of remote sensing images is the acquisition of actual land cover category data since it is difficult and expensive. We need comprehensive surveys over the area of interest, and the labelling task must be performed by experts in related fields. In literature, there have been some supervised methods with active learning to promote the efficiency of classification and change detection, because active learning is helpful to overcome the lack of labelled samples. Begüm et al proposed an active learning technique developed in the framework of the Bayes’ rule for compound classification (Begüm D., 2012). It selected the unlabelled pixels that were classified with the maximum uncertainty which was assessed by joint entropy. This algorithm tried to use statistical methods to classify the land cover categories in remote sensing images under certain conditions. Furlani et al used the support vector data description to identify the most relevant training samples in classification, and selected unlabelled samples by adopting diversity criterion with the support vector data description classifier (Furlani M., 2012). However, the effectiveness of support vector data description highly depended on the quantity and quality labelled samples used to define the enclosing hypersphere. So diversity criterion were needed to select candidate training pixels. This kind of method was not adequate for large scale problems, since the computation complexity of support vector machine was very high. Jefersson et al proposed an interactive classification of remote sensing images considering multiscale segmentation (Jefersson, 2013). They used a boosting-based active learning strategy to select regions at the most appropriate scales of representation and found that the combination of scales produces better results than isolated scales in a relevance feedback process. Moumita et al presented a change detection technique (Moumita, 2014) using neural networks in active learning, and the network was iteratively trained with labelled patterns, using the query functions: uncertainty sampling and query-by-committee. However, the optimal parameters are hard to find but play important roles in neural network. Kiran et al presented a cohesive algorithm for image classification and ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume III-7, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. doi:10.5194/isprsannals-III-7-141-2016 141 change detection based on active learning which tackled the lack of actual land cover category data to detect deforestation (Kiran, 2014). They used Expectation Maximization algorithm as a pre-clustering, an active learning classification based on maximum likelihood estimation, and an automatic threshold of pair-wise PCA as post-classification comparison. Xiao et al used the structural feature description combined with both local and global information and query expansion for object detection (Xiao B., 2014). They converted the detection task to a ranking query task by using a ranking support vector machine for the object detection in very high resolution remote sensing images. In literature, only few approaches for detecting land cover category transitions by supervised techniques with active learning have been presented. Active learning is a sample selection strategy which can be used in many kinds of classification algorithms. The difference between them is the classification algorithm, and what we need to do is designing an appropriate criterion for them respectively. In this study, we presented an interactive change detection method for high resolution remote sensing images under the framework of Alexander’s method (Alexander F., 2012), which used active learning to overcome the shortages of existing remote sensing image change detection techniques. There is no need for any annotation of actual land cover category at the beginning in our method. First, we use unsupervised way to find a certain number of the most representative objects in the first iteration and label them with “change” or “no change” by user as the initial training set. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection result doesn’t change notably. Specifically, select the most representative sample in each iteration, label it with “change” and “no change”, add this labelled sample into training set, and delete it from the testing set. Repeat this process until the result meets the requirement. In our method, the manual annotation can be reduced substantially, and a desirable detection result can be obtained in a few iterations. This paper is organized as follows. Section 2 presents the interactive change detection algorithm based on active learning in detail, and gives a specific interpretation for every step in the proposed method. Some representative experimental results are exhibited in section 3. Finally, Section 4 draws the conclusion of this work. 2. INTERACTIVE CHANGE DETECTION BASED ON ACTIVE LEARNING This work of interactive change detection use active learning to construct an efficient training set in order to make full use of various information in high resolution remote sensing images. We use the original images without any annotation to select the most representative samples, and obtain the results meeting the requirements gradually by adding those representative samples into training set after labelling them manually. Repeat this process until reach a satisfactory detection result. The whole framework flow chart is shown in Figure 1. And each step of this interactive change detection for high resolution remote sensing images based on active learning will be introduced in the following parts. Input Superpixel Segmentation