Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery
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ORIGINAL PAPER
Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery Mahmoud Salah 1 Received: 8 May 2020 / Accepted: 18 October 2020 # Società Italiana di Fotogrammetria e Topografia (SIFET) 2020
Abstract Robust approaches for image change detection (ICD) are essential for a range of large-scale applications. However, the uncertainties involved in such approaches have not been fully addressed. To investigate this problem, this paper proposes a new approach for change detection from multi-temporal very high resolution (VHR) satellite imagery based on uncertainty detection and management. First, two GeoEye-1 images of Giza urban area (Egypt), acquired in 2009 and 2019, have been geographically co-registered and their histograms have been matched. Second, a set of feature attributes have been generated from the coregistered images. Third, the support vector machine (SVM) algorithm has been adopted to classify the data into four classes: building, tree, road, and ground. In this regard, the co-registered images along with the generated attributes have been applied as input data for the SVM to calculate the probability of each pixel belonging to each class. After that, the probability images for both epochs have been compared to model the uncertainty of changes. The uncertainty places are then evaluated to estimate their likelihood of being change or no change. Finally, the obtained results have been compared with manually digitized change detection map. Compared with using the widely used post-classification comparison (PCC) approach, the results suggest that (1) the proposed method has improved the overall accuracy of change detection by 13%; (2) the class-accuracies have been improved by 35.63%; and (3) the achieved accuracies for the proposed approach are less variable. Whereas the standard deviation (SD) of the accuracies obtained for the proposed approach is 6.80, the SD of those obtained for the PCC approach is 35.50. Keywords Multi-temporal . Uncertainty . Classification . SVM . Change detection
Introduction and literature review Image change detection (ICD) is the process that automatically compares and correlates two sets of images of the same region collected at different times to identify differences between them (Choi et al. 2009). ICD is an important tool for many large-scale applications such as assessment of risks and damages induced by earthquakes (Xu et al. 2010), monitoring of environment (Lhermitte et al. 2011), monitoring of disasters (Shi and Hao 2013), and updating large area land use/ cover (LULC) maps (Mas et al. 2017). ICD has been considered using field observation, airborne light detection and ranging (LiDAR), remotely sensed data, or a combination of these techniques (Mora et al. 2018).
* Mahmoud Salah [email protected] 1
Department of Surveying Engineering, Faculty of Engineering Shoubra - Benha University, 108 Shoubra St, Cairo, Egypt
Remotely sensed data have been widely used for detecting LULC changes because of the frequently imp
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