836 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 9, NO. 5, SEPTEMBER 2012 Multitemporal Image Change Detection Using a Detail-Enhancing Approach With Nonsubsampled Contourlet Transform Shutao Li, Member, IEEE, Leyuan Fang, Student Member, IEEE, and Haitao Yin Abstract—In this letter, we propose an unsupervised approach for change detection in multitemporal satellite images based on a novel detail-enhancing algorithm. The multitemporal source images are first used to generate the difference image, which is decomposed into low-pass approximation and high-pass direc- tional subbands by the nonsubsampled contourlet transform. The coefficients from the directional subbands are fused at intrascale and interscale to extract the meaningful details of the difference image. After that, the extracted details are injected into one base image selected from the approximation subbands, which results in a detail-enhanced difference image. For each pixel in the enhanced difference image, a dimension-reduced feature vector is created using the principal component analysis (PCA). The final change detection map is achieved by clustering the feature vectors using a PCA-guided k-means algorithm into “changed” and “unchanged” classes. Experimental results demonstrate the superior performance of the proposed approach compared with several well-known change detection techniques. Index Terms—Change detection, detail-enhancing strategy, dif- ference image, nonsubsampled contourlet transform (NSCT), principal component analysis (PCA)-guided k-means. I. I NTRODUCTION C HANGE detection directly compares a pair of input im- ages acquired on the same geographical area at different times to identify changes that may have occurred. In recent years, fast and accurate detection of a changed region has played a very essential role in many applications, such as urban studies, disaster management, and agricultural surveys. The existing approaches for detecting changes can be cate- gorized as either supervised or unsupervised. Compared with the supervised techniques, the unsupervised counterparts [1]– [5] do not require the availability of a ground truth or any other additional information for the classifier training, thus attracting more interests [5]. Most of the unsupervised methods are de- veloped based on the analysis of the difference image, which can be created by calculating the difference of the input images [1]–[5]. In [1], Bruzzone and Prieto introduce two automatic techniques for change detection using the difference image. In Manuscript received October 25, 2011; revised December 3, 2011 and December 24, 2011; accepted December 28, 2011. Date of publication February 21, 2012; date of current version May 29, 2012. This work was sup- ported in part by the National Natural Science Foundation of China under Grant 61172161, by the Scholarship Award for Excellent Doctoral Student granted by the Chinese Ministry of Education, and by the Fundamental Research Funds for the Central Universities, Hunan University. The authors are with the College of Electrical and Information Engineering, Hunan University, Changsha 410082, China (e-mail: shutao_li@yahoo.com.cn; fangleyuan@gmail.com; ocean_waves@126.com). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/LGRS.2011.2182632 Fig. 1. Structure of the NSCT with two levels of decomposition and eight and four directional subbands in the first and second scales, respectively. [2], Celik proposes a multiscale Bayesian framework which performs the expectation-maximization (EM)-based approach over the subbands resulted from the dual-tree complex wavelet transform (DT-CWT) decomposition of the difference image. In general, these approaches provide impressive results for detecting the changes between the two input images. However, all these techniques model the changed and unchanged classes in the difference image by the simple Gaussian assumption, and therefore, their performances often deteriorate for real images. Recently, an efficient change detection method [3] has been proposed through the principal component analysis (PCA) and the k-means clustering. The PCA is employed for extracting the feature vectors, which are forwarded to the k-means clus- tering to compute the final change detection result. In [4], the feature vector is extracted from the subbands created by the undecimated discrete wavelet transform (UDWT), and the final changes are detected by applying the k-means algorithm on the extracted feature vectors as well. Overall, both the afore- mentioned two methods perform well when the changes in the difference image are distinct. However, they fail to characterize geometrical details of the difference image sufficiently, thus resulting in improper detection around the detailed regions. Moreover, since the k-means algorithm used in these methods will easily get stuck in local optima, they are prone to produce false detections without a good initial centroid. In order to address the aforementioned limitations, we propose a novel unsupervised method for change detection in multitemporal images, which is based on a new detail- enhancing strategy. In this strategy, the difference image is first decomposed into one low-pass approximation subband and multiple high-pass directional subbands at each scale using the nonsubsampled contourlet transform (NSCT) [6]. Through the intrascale and interscale fusion on the directional subbands, the meaningful details of the difference image can be extracted. Then, the extracted details are merged into one selected base 1545-598X/$31.00 © 2012 IEEE