A novel multiple change detection approach based on tri-temporal logic-verified change vector analysis in posterior probability space

Abstract

• TLCVAPS gets rid of the dependence on change class information that contained in training samples, reducing the cost of sample acquisition in multiple change detection problems. • TLCVAPS avoids the error accumulation caused by comparing classification results at different dates for change trajectory detection. • TLCVAPS is the first attempt to introduce temporal logic into multiple change detection tasks, which is a pilot research for the utilization of time information. Detailed land cover change trajectory offers a better opportunity for understanding the dynamic of land surface process. However, change information contained in training samples, which are usually difficult to obtain, needs to be provided in advance to achieve such goal within a complex scenario. A novel multiple change detection approach, namely Tri-temporal Logic-verified Change Vector Analysis (TLCVA) in posterior Probability Space (TLCVAPS), was proposed in this paper. It removes the dependence on change class information contained in training samples, while reducing the detection errors by taking change logic into account for the first time. The proposed approach consists of three steps, including: (1) change vector produced in posterior probability space, (2) binary change detection via TLCVA, and (3) change trajectory identification through combining change vector angle comparison and logic verification in change pattern. We applied the proposed approach on three tri-temporal datasets obtained in Nanjing, Xianning, and Zhenjiang, China, respectively. The results confirmed the superiority of TLCVAPS in comparison with state-of-the-art multiple change detection methods with the same prior knowledge.