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SENSOR LETTERS Vol. 10, 1–10, 2012
A Comparison of Two Object-Oriented Methods for Land-Use/Cover Change Detection with SPOT 5 Imagery Jie Liang1 , Jianyu Yang1 ∗ , Chao Zhang1 , Xuejiao Du1 , Anzhi Yue1 , and Dongping Ming2 1
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2 School of Information Engineering, China University of Geosciences, Beijing 100083, China (Received: 30 June 2011. Accepted: 20 September 2011)
Change detection techniques based on high-spatial resolution imageries have been widely applied in environment monitoring, land management, dynamic monitoring of the military battlefield. In this paper, two methods including (1) object-oriented change detection based on post-classification comparison (CDBPC), (2) object-oriented change detection based on multi-feature (CDBMF) were put forward and compared to determine which method was more effective. The ample spectral information, textural information, structure information of high-spatial resolution SPOT 5 imageries were utilized synthetically in both two methods. In contrast to CDBPC, CDBMF did change classification only once, thus it avoided the accumulated classification error. Accuracy assessment shows that CDBMF is more favorable for land-use/cover change detection, and the overall accuracy has been improved significantly from 80.00% to 86.67%.
Keywords: Remote Sensing, Change Detection, Object-Oriented, Image Segmentation, MultiFeature.
Change detection is the process of indentifying differences in the state of an object or phenomenon by observing it at different times.1 Timely and accurate change detection of Earth’s surface features provides the foundation for better understanding relationships and interactions between human and natural phenomena to better manage and use resources.2 Nowadays, change detection is widely used in environment monitoring, land management, dynamic monitoring of the military battlefield.3 Remote Sensing, especially high-spatial resolution multispectral imagery from satellite and aerial sensors (e.g., IKONOS from GeoEye, Inc., QuickBird from DigitalGlobe, Inc., ADS40 from Leica Geosystems, Inc.), which offers abundance information of the earth surface, is a valuable tool for addressing the aforementioned applications.4–5 However, traditional change detection methods based on per-pixel analysis may not function successfully with highspatial resolution image.6 Their common characteristic is only using the statistical information of pixel value and hardly analyzing the shape feature and structure feature of objects.7 Another serious problem found in per-pixel approaches is the ‘salt-and-pepper’ phenomenon in change ∗
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Sensor Lett. 2012, Vol. 10, No. xx
detection results, which is meaningless for application. In some degree, the improvement of change detection techniques falls behind the development of sensor hardware technology out and away. To overcome these problems, objected-oriented change detection methods have been put forward and focused by the international research field (e.g., Hazel et al. 2001, Hall et al. 2003, Hay et al. 2005).8–10 Object-oriented analysis subdivides the image into meaningful homogeneous regions based not only on spectral properties but also on shape, texture, size, and other topological features, and organizes them hierarchically as image objects.6 There is great interest in land-use/cover change detection using high-spatial resolution multispectral imagery for object-oriented change detection. Up to now, many researchers have been proposed lots of object-oriented change detection methods. For example, Walter (2004)11 performed an object-based post-classification comparison change detection using pre-existing objects in a GIS database. His approach is based on a supervised maximum likelihood classification. Blaschke (2005) dealt with the problems associated with multi-temporal object recognition using a post-classification comparison method and proposed a framework for image object-oriented change detection. Desclee et al (2006)12 proved the effectiveness of object-based change detection capability in detecting
1546-198X/2012/10/001/010
doi:10.1166/sl.2012.1865
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RESEARCH ARTICLE
1. INTRODUCTION
