Abstract
Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resolution (VHR) imagery and determining changes in small and complex objects such as buildings or roads, traditional methods showed limitations, for example, the large number of false alarms or noise in the results. Thus, researchers have focused on extending PBCD to OBCD. In this study, we proposed a method for detecting the newly built-up areas by extending PBCD results into an OBCD result through the Dempster–Shafer (D–S) theory. To this end, the morphological building index (MBI) was used to extract built-up areas in multitemporal VHR imagery. Then, three PBCD algorithms, change vector analysis, principal component analysis, and iteratively reweighted multivariate alteration detection, were applied to the MBI images. For the final CD result, the three binary change images were fused with the segmented image using the D–S theory. The results obtained from the proposed method were compared with those of PBCD, OBCD, and OBCD results generated by fusing the three binary change images using the major voting technique. Based on the accuracy assessment, the proposed method produced the highest F1-score and kappa values compared with other CD results. The proposed method can be used for detecting new buildings in built-up areas as well as changes related to demolished buildings with a low rate of false alarms and missed detections compared with other existing CD methods.
Highlights
The most important social transformation in human history is regarded as urbanization, in which cities play an important role [1]
We proposed a method for object-based building Change detection (CD) by using D–S theory to fuse multiple pixel-based change detection (PBCD) results with the segmented image
morphological building index (MBI) feature images were generated from the multitemporal VHR imagery
Summary
The most important social transformation in human history is regarded as urbanization, in which cities play an important role [1]. Only 3% of the Earth’s land is occupied by cities, 3.5 billion people live in cities, and it will rise to over 5 billion by 2030, making it 95% of the urban expansion. This rapid urbanization results in 60–80% of energy consumption and carbon emissions [2,3]. The United Nations Sustainable Development Goals include sustainable cities and communities (i.e., Goal 11) that require a focus on safe and sustainable human settlement [4]. Most of the sustainable development goals are related to urban decision making [5]
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