A case study for a multitemporal segmentation approach in optical remote sensing images.

Continuous observations from remote sensors provide high temporal and spatial resolution imagery, and better remote sensing image segmentation techniques are mandatory for efficient analysis. Among them, one of the most applied segmentation techniques is the region growing algorithm. Within this context, this paper describes a study case for a multitemporal segmentation that adapts the traditional region growing technique. Our method aims to detect homogeneous regions in space and time observing a sequence of optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping distance as the homogeneity criterion to grow regions. A case study on high temporal resolution for sequences of Landsat-8 vegetation indices products provided satisfactory outputs.GEOProcessing 2018

Segmentation of optical remote sensing images for detecting homogeneous regions in space and time.

With the amount of multitemporal and multiresolution images growing exponentially, the number of image segmentation applications is recently increasing and, simultaneously, new challenges arise. Hence, there is a need to explore new segmentation concepts and techniques that make use of the temporal dimension. This paper describes a spatio-temporal segmentation that adapts the traditional region growing technique to detect homogeneous regions in space and time in optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping measure as the homogeneity criterion. Study cases on high temporal resolution for sequences of MODIS and Landsat-8 OLI vegetation indices products provided satisfactory outputs and demonstrated the potential of the spatio-temporal segmentation method.Também publicado na Revista Brasileira de Cartografia, v. 70, n. 5, p. 1779-1801, 2018. Special Issue XIX Brazilian Syposium on GeoInformatics, 2018. DOI: 10.14393/rbcv70n5-45227