2017 conference paper
Hierarchical change detection framework for biomass monitoring
2017 ieee international geoscience and remote sensing symposium (igarss), 620β623.
In this paper, we present a nearest neighbor based hierarchical change detection methodology for analyzing multi-temporal remote sensing imagery. A key contribution of this work is to define change as hierarchical rather than boolean. Based on this definition of change pattern, we developed a novel time series similarity based change detection framework for identifying inter-annual changes by exploiting phenological properties of growing crops from satellite time series imagery. The proposed framework consists of four components: hierarchical clustering tree construction, nearest neighbor based classification, relaxation labeling, and change detection using similarity hierarchy. Though the proposed approach is unsupervised, we present evaluation using manually induced change regions embedded in the real dataset. We compare our method with the widely used K-Means clustering and evaluation shows that K-Means over-detects changes in comparison to our proposed method.