Fish-pond change detection based on short term time series of RADARSAT images and object-oriented method

As an important type of wetlands, fish-pond provides a number of important ecosystem services. It was easily affected by human activities such as the change of cultivation structure. Tracking the fast change of fish-pond can obtain the information that the environment is impacted by human activities to what extent. In this study, we proposed a change detection procedure in order to delineate fast change fish-pond using high resolution, short term time series of SAR imagery. The procedure includes (1) SAR images preprocessing, (2) object extraction, (3) multi-temporal change characteristics analysis of fish-pond, (4) change objects classification, and (5) a final change results analysis. In this study, object-oriented method was adopted to extract and analyze the texture characteristics of fish-pond in the high spatial resolution of SAR data. Objects were generated with suitable scale parameters by means of multi-temporal segmentation approach according to the image features (including backscatter, difference and ratio of three temporal RADARSAT images, shape characters), and the changed objects were extracted through analyzing the shape index and contextual information generated by Definiens software. Six change types of fish-pond were achieved. The results indicate that high resolution SAR data can provide better information for fish-pond change detection in the study area, and the procedure proposed in this study can efficiently obtain the fast change objects with high accuracy between three short term time series SAR images, which are useful for the further classification.