Identifying dynamic changes with noisy labels in spatial-temporal data: A study on large-scale water monitoring application

The need for effective change detection is ever growing with more emerging large-scale spatial-temporal datasets that contain gridded time series data. To detect meaningful changing events with respect to our desired characteristics, in this paper we focus on the post-classification change detection problem which aims to apply change detection techniques on the time series of classification outputs. To study the challenges and to evaluate the performance, we apply the change detection techniques to an application of water monitoring using remote sensing data. Since the learning model can be affected by special properties of remote sensing data, the obtained classification outputs usually contain much noise. Therefore the successful change detection requires an elaborate mechanism to handle the time series of noisy classification outputs. To this end we propose to integrate spatial and temporal constraints into an optimization based change detection framework. The proposed framework mitigates the noise in the time series and can be efficiently solved by an EM-style algorithm. The extensive experimental results on both synthetic and real-world datasets very well demonstrate the effectiveness of the proposed method in detecting the water dynamics.