Crop Recognition in Tropical Regions based on spatio-temporal Conditional Random Fields from multi-temporal and multi-resolution sequences of remote sensing images

Author: Pedro Marco Achanccaray Diaz

Original title: Reconhecimento de culturas em regiões tropicais baseado em campos aleatórios condicionais espaço-temporais a partir de sequências de imagens de sensoriamento remoto multi-temporais e de múltiplas resoluções.

Language: English


The earth population growth has continuously increased the demand for agricultural production. Thus, acreage and crop yield information becomes increasingly important. Techniques based on satellite images are one of the most attractive options for agricultural monitoring over large areas. Most of the scientific works on this application were developed for temperate regions of the planet, which present a much simpler dynamics than those in tropical regions.
In this context, the present thesis proposes a new automatic method based on Conditional Random Fields (CRF) for the crop recognition in tropical regions from multi-temporal and multi-resolution image sequences from different orbital sensors.
Experiments were performed to validate several variants of the proposed method. We used public databases from two regions of Brazil that comprise sequences of optical and radar images with different spatial resolutions.
The experiments demonstrated that the proposed method achieved higher accuracy than methods based on a single image or sensor. Particularly, the reduction of the \textit{salt-and-pepper} effect in the generated maps was noticed due, mainly, to the capacity of the method to capture contextual information.