A temporal-spectral value and shape change detection method integrating thematic index information and spectral band information

Satellite imagery time series change detection methods are effective in avoiding pseudochange due to vegetation phenology to a certain extent. Traditional time series change detection methods use thematic indexes (e.g., NDVI, RVI) to obtain time series information for corresponding change detection. However, change detection methods using several thematic index time series may not make full use of other spectral band information in remotely sensed images and may still suffer from over- and under-detections. To address this challenge, a temporal-spectral value and shape change detection method integrating thematic index information and spectral band information (TISB) is proposed. Possible clouds and cloud shadowing phenomena are removed according to the changes in the spectral values of the remotely sensed images to avoid the generation of pseudochanges in clouds. The spectral and time series information is used to obtain change information from the value perspective, and then, further possible enhanced change regions from a shape perspective to obtain the final change detection results through the expectation–maximization (EM) method. Experiments with Landsat images have shown that the TISB method improves detection results by approximately 1–4% compared to the comparison method.