An integrated framework for assessing the accuracy of geobia landcover products.

Se establece un metodología para evaluar la cartografía de capas GIS

A GEOBIA methodology for fragmented agricultural landscapes

Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Classificação dos tipos de pavimentos das vias urbanas a partir de imagem de alta resolução espacial por meio de análise orientada a objeto

Pós-graduação em Ciências Cartográficas - FCT

Novel parameters for evaluating the Spatial and Thematic accuracy of land cover maps.

Se proponen novedosas fórmulas para evaluar la certeza de la cartografía

Satellite based multi-scale methods to support governance of Brazil's low-carbon agriculture (ABC Plan).

Brazil's Low Carbon Agriculture is one the initiatives that puts the climate in the agricultural agenda towards a more sustainable and adapted agriculture under global changes. Among the several practices listed and supported by the ABC Plan, zero tillage and integrated crop-livestock-forestry systems including the recovery of degraded pasture are the most relevant ones. The objective of this paper is to present the Geo-ABC Project, a procedure to monitor the implementation of the Brazil?s Low Carbon Agriculture (ABC Plan) and aiming at the development of remote sensing methods to monitor agricultural systems listed in the ABC Plan and adopted at local scale.