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

Using Landsat 8 image time series for crop mapping in a region of Cerrado, Brazil.

Abstract: The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification

Identification of gaps in sugarcane plantations using UAV images.

The objective of this study is to present a methodology for the detection and quantification of gaps formed during planting or growing of sugarcane crops. The use of UAV images for precision agriculture is relevant because it brings new possibilities for improving crop's productivity by feeding the producer with highly accurate data about the crop status