Abstract
Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research.
Highlights
We summarize and make a reasonable prediction on the research trend of urban change detection in the future
The first attempt can be found in change vector analysis (CVA) [45], which converts the difference of pixel values into the difference of feature vectors
It must be noted that the similar CVs extracted from the pixel-wise algebraic calculation should be clustered in the last step, the artificial setting of thresholds is an unavoidable problem in the algebraic method
Summary
Change detection based on remote sensing (RS) technology realizes the process of quantitatively analyzing and determining the change characteristics of the surface from multi-temporal images [1]. It should be pointed out that “multi-objective” in our article emphasizes the diversity of scenario categories in which the change subjects belong rather than the number of targets This requirement leads to critical elements of the change detection framework, that is, refining the basic processing unit and optimizing extracted results. To make sense of the relevance between RS data and changing feature acquisition, the most commonly used datasets obtained by the multi-source satellite sensors in change detection are summarized, including SAR images, medium- and high-resolution multispectral images, hyperspectral images, and even the heterogeneous images On this basis, the attributes of the datasets are deeply discussed, including their application scenarios and restrictions. According to the detection framework, the literatures about change detection in the past five years are analyzed Their detailed technical points are divided into three independent parts, namely feature extraction, data fusion, and multi-objective scenario analysis, demonstrated, Section 4, and Section 5, respectively. We summarize and make a reasonable prediction on the research trend of urban change detection in the future
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