From variant index images (like NDVI) derived from time-serial remote sensing images, both the ‘spatial variation’ and ‘temporal variation’ for multiple disciplines could be defined, which are widely available, in particular, in the literatures. However, most of those research results show high uncertainty and low reliability.
- Examples of spatial and temporal variations of some fine-grained suspended particle characteristics in two Danish coastal water bodies (by Ole Aarup Mikkelsen 2001)
- Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 (by Liming Zhou el Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 - Resear…)
- Annual integral changes of time serial NDVI in mining subsidence area (Ma Chao el 2011)
In practice, in order to *accurately* define spatial variations or detect spatial changes directly from time-serial images or index images, many technical challenges are required to be solved.
For example, in GIS and land management, how *effectively* to detect spatial changes of landcover over time (i.e., landuses, building lots, fences, tree crowns, etc.)?
Obviously, algorithms for change detection /defining spatial variation are mostly different from feature extraction, including traditional change detection algorithms and object-oriented algorithms.
For last few years, many researches and practitioners have been discussing object-oriented Change Detection with eCognition and ERDAS Objective.
With eCognition, it uses the multivariate alteration detection (MAD) transformation (by Allan et al, 1998; Nielsen & Conradsen, 1997), which is based on the established canonical correlations analysis.
However, it looks that MAD might be challenging, as a ‘real' object-oriented solution for Change Detection in accurate way.
Inversely, ERDAS Objective uses Discriminant Function Change algorithm to help extract change features, which demonstrates direct and efficient way to map ‘spatial variation’ and perform change detection...