A Multi-Feature Hyper-Temporal Change Vector Analysis Method for Change Detection in Multi-Annual Time Series of HR Satellite Images

A great effort has been put on developing technologies that can process High Resolution (HR) satellite datasets to properly monitor the environmental changes and produce long term Change Detection (CD) maps. However, there is still a need to design CD approaches that process Satellite Image Time Series (SITS) with high spatial, spectral, and temporal resolution and describe changes that have occurred between the consecutive years. Here, a CD processing chain is proposed that: i) extracts several relevant features of the spectral trends of different sets of LC changes, ii) produces a regular and dense feature time series, iii) analyzes differences between the consecutive years by using a Multi-feature Hyper-temporal Change Vector Analysis (MHCVA) technique, and iv) detects the year and the probability of changes at pixel level. The effectiveness of the proposed approach is tested on a multi-annual Landsat 7 and 8 images of an area located in Amazon.