Resumo:Esse artigo apresenta um método de filtragem de nuvem de pontos LASER para obtenção de um Modelo Digital de Terreno (MDT). O processo de filtragem é realizado com base em uma superfície aproximada gerada a partir de pontos amostrados sobre vias urbanas. Esses pontos são determinados por meio de linhas detectadas na imagem de intensidade do pulso laser via detector de Steger. A principal suposição do método é que o terreno tem um comportamento suave no interior das quadras e, dessa forma, os pontos amostrados ao longo das vias permitem, utilizando o método de interpolação por krigagem, uma representação adequada do terreno no interior das quadras, ou seja, relativamente próxima dos pontos LASER de terreno dessas regiões. Assim, a filtragem é realizada pela verificação da proximidade dos pontos da nuvem LASER original com a superfície aproximada gerada. Por fim, um MDT é obtido da nova amostra pelo método de interpolação por krigagem, melhorando a descrição da superfície. A partir dos experimentos realizados foi possível verificar a viabilidade do método proposto, com resultados de boa coerência visual e indicadores numéricos satisfatórios.
Palavras-chave: MDT, Filtragem, Krigagem.
Abstract:This paper presents a method of filtering point clouds generated by laser scanning, to obtain a Digital Terrain Model (DTM). The filtering process is performed based on an approximated
This paper proposes a method for extracting groups of straight lines that represent roof boundary sides and roof ridgelines from high-resolution aerial images using corresponding airborne laser scanner (ALS) roof polyhedrons as initial approximations. Our motivation for this research is the possibility of future use of resulting image-space straight lines in several applications. For example, straight lines that represent roof boundary sides and precisely extracted from a high-resolution image can be back-projected onto the ALS-derived building polyhedron for refining the accuracy of its boundary. The proposed method is based on two main steps. First, straight lines that are candidates to represent roof ridgelines and roof boundary sides of a building are extracted from the aerial image. The ALS-derived roof boundary sides and roof ridgelines are projected onto the image space, and bolding boxes are constructed around the projected straight lines while considering the projection errors. This allows the extraction of straight lines within the bounding boxes. Second, a group of straight lines that represent roof boundary sides and roof ridgelines of a selected building is obtained through the optimization of a Markov random field-based energy function using the genetic algorithm optimization method. The formulation of this energy function considers several attributes, such as the proximity of the extracted straight lines to the corresponding projected ALS-derived roof polyhedron and the rectangularity (extracted straight lines that intersect at nearly 90 o ). In order to validate the proposed method, four experiments were accomplished using high-resolution aerial images, along with interior and exterior orientation parameters, and available ALS-derived building roof polyhedrons. The obtained results have shown that the method works properly and this will be qualitatively and quantitatively demonstrated in this research.
ABSTRACT:In this paper a method for automatic extraction of building roof boundaries is proposed, which combines LiDAR data and highresolution aerial images. The proposed method is based on three steps. In the first step aboveground objects are extracted from LiDAR data. Initially a filtering algorithm is used to process the original LiDAR data for getting ground and non-ground points. Then, a region-growing procedure and the convex hull algorithm are sequentially used to extract polylines that represent aboveground objects from the non-ground point cloud. The second step consists in extracting corresponding LiDAR-derived aboveground objects from a high-resolution aerial image. In order to avoid searching for the interest objects over the whole image, the LiDAR-derived aboveground objects' polylines are photogrammetrically projected onto the image space and rectangular bounding boxes (sub-images) that enclose projected polylines are generated. Each sub-image is processed for extracting the polyline that represents the interest aboveground object within the selected sub-image. Last step consists in identifying polylines that represent building roof boundaries. We use the Markov Random Field (MRF) model for modelling building roof characteristics and spatial configurations. Polylines that represent building roof boundaries are found by optimizing the resulting MRF energy function using the Genetic Algorithm. Experimental results are presented and discussed in this paper.
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