Orientador: Aluir Porfírio Dal Poz / Resumo: É esperado que o operador humano permaneça, por um longo tempo, como parte integrante do sistema de extração de feições. Portanto, as pesquisas que caminham para o desenvolvimento de novos métodos semi-automáticos são ainda de grande importância. Nesta linha, esta pesquisa propõe um método semi-automático para a extração de rodovias em imagens digitais. A metodologia é uma combinação entre técnicas de correlação e estratégia de teste ativo. Os resultados experimentais obtidos da aplicação do método em imagens reais mostram que o método funciona corretamente, demonstrando que pode ser usado em esquemas de captura de dados. / Abstract: The human operator is still expected to remain as part of the feature extraction system for a relative long time. Therefore, researches for the development of new semi-automatic methods is still of great importance. Following this line, this research proposes a semi-automatic method for road extraction from digital images. It is based on a combination between correlation techniques and an active testing strategy. In order to initialize the extraction process, the operator needs to supply two close seed points plus another one at the end of road segment selected to be extracted. Experimental results obtained from the application of the method to real image data show that the method works properly, demonstrating that the developed method can be used in data capturing schemes. / Mestre

Accurate and up-to-date road information is essential for both effective urban planning and disaster management. Today, very high resolution (VHR) imagery acquired by airborne and spaceborne imaging sensors is the primary source for the acquisition of spatial information of increasingly growing road networks. Given the increased availability of the aerial and satellite images, it is necessary to develop computer-aided techniques to improve the efficiency and reduce the cost of road extraction tasks. Therefore, automation of image-based road extraction is a very active research topic. This thesis deals with the development and implementation aspects of a semi-automatic road extraction strategy, which includes two key approaches: multidirectional and single-direction road extraction. It requires a human operator to initialize a seed circle on a road and specify a extraction approach before the road is extracted by automatic algorithms using multiple vision cues. The multidirectional approach is used to detect roads with different materials, widths, intersection shapes, and degrees of noise, but sometimes it also interprets parking lots as road areas. Different from the multidirectional approach, the single-direction approach can detect roads with few mistakes, but each seed circle can only be used to detect one road. In accordance with this strategy, a RoadModeler prototype was developed. Both aerial and GeoEye-1 satellite images of seven different types of scenes with various road shapes in rural, downtown, and residential areas were used to evaluate the performance of the RoadModeler. The experimental results demonstrated that the RoadModeler is reliable and easy-to-use by a non-expert operator. Therefore, the RoadModeler is much better than the object-oriented classification. Its average road completeness, correctness, and quality achieved 94%, 97%, and 94%, respectively. These results are higher than those of Hu et al. (2007), which are 91%, 90%, and 85%, respectively. The successful development of the RoadModeler suggests that the integration of multiple vision cues potentially offers a solution to simple and fast acquisition of road information. Recommendations are given for further research to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use.

Accurate and up-to-date road information is essential for both effective urban planning and disaster management. Today, very high resolution (VHR) imagery acquired by airborne and spaceborne imaging sensors is the primary source for the acquisition of spatial information of increasingly growing road networks. Given the increased availability of the aerial and satellite images, it is necessary to develop computer-aided techniques to improve the efficiency and reduce the cost of road extraction tasks. Therefore, automation of image-based road extraction is a very active research topic. This thesis deals with the development and implementation aspects of a semi-automatic road extraction strategy, which includes two key approaches: multidirectional and single-direction road extraction. It requires a human operator to initialize a seed circle on a road and specify a extraction approach before the road is extracted by automatic algorithms using multiple vision cues. The multidirectional approach is used to detect roads with different materials, widths, intersection shapes, and degrees of noise, but sometimes it also interprets parking lots as road areas. Different from the multidirectional approach, the single-direction approach can detect roads with few mistakes, but each seed circle can only be used to detect one road. In accordance with this strategy, a RoadModeler prototype was developed. Both aerial and GeoEye-1 satellite images of seven different types of scenes with various road shapes in rural, downtown, and residential areas were used to evaluate the performance of the RoadModeler. The experimental results demonstrated that the RoadModeler is reliable and easy-to-use by a non-expert operator. Therefore, the RoadModeler is much better than the object-oriented classification. Its average road completeness, correctness, and quality achieved 94%, 97%, and 94%, respectively. These results are higher than those of Hu et al. (2007), which are 91%, 90%, and 85%, respectively. The successful development of the RoadModeler suggests that the integration of multiple vision cues potentially offers a solution to simple and fast acquisition of road information. Recommendations are given for further research to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use.

Made available in DSpace on 2014-06-11T19:23:30Z (GMT). No. of bitstreams: 0 Previous issue date: 2005Bitstream added on 2014-06-13T20:10:57Z : No. of bitstreams: 1 mendes_tsg_me_prud.pdf: 3488297 bytes, checksum: edcf0b9a2b1e54f8b59d3dd8474a32a0 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / É esperado que o operador humano permaneça, por um longo tempo, como parte integrante do sistema de extração de feições. Portanto, as pesquisas que caminham para o desenvolvimento de novos métodos semi-automáticos são ainda de grande importância. Nesta linha, esta pesquisa propõe um método semi-automático para a extração de rodovias em imagens digitais. A metodologia é uma combinação entre técnicas de correlação e estratégia de teste ativo. Os resultados experimentais obtidos da aplicação do método em imagens reais mostram que o método funciona corretamente, demonstrando que pode ser usado em esquemas de captura de dados. / The human operator is still expected to remain as part of the feature extraction system for a relative long time. Therefore, researches for the development of new semi-automatic methods is still of great importance. Following this line, this research proposes a semi-automatic method for road extraction from digital images. It is based on a combination between correlation techniques and an active testing strategy. In order to initialize the extraction process, the operator needs to supply two close seed points plus another one at the end of road segment selected to be extracted. Experimental results obtained from the application of the method to real image data show that the method works properly, demonstrating that the developed method can be used in data capturing schemes.

Made available in DSpace on 2014-06-11T19:23:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2004Bitstream added on 2014-06-13T20:10:58Z : No. of bitstreams: 1 zanin_rb_me_prud.pdf: 6131122 bytes, checksum: c6c482e83836f2b30cf13b6d9d31f02a (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Pesquisas em extração de rodovias em imagens digitais não são recentes, sendo as primeiras da década de 70. Os métodos desenvolvidos normalmente são classificados em duas classes: semi-automático e automático. Esta última classe não prevê a intervenção de um operador. Neste contexto, a extração automática de cruzamentos de rodovias é fundamental, embora muito poucos trabalhos são encontrados na literatura sobre extração de rodovias. A razão principal é a grande diversidade de cruzamentos de rodovias, gerando algumas dificuldades para construir um conhecimento a priori sobre cruzamentos. Esta pesquisa propõe uma metodologia automática para resolver este problema, combinando segmentos de rodovias extraídos de uma imagem de alta resolução (pixel < 0,7 m), com linhas extraídas numa imagem de baixa resolução (pixel > 2m), reamostrada da imagem original de alta resolução. A metodologia proposta foi testada em um conjunto de imagens de alta resolução, mostrando-se confiável e eficiente. Desde que as bordas das rodovias sejam satisfatoriamente definidas, o método se mostrou capaz de extrair totalmente os cruzamentos de rodovias. Além disso, gerou uma significante melhora na malha viária (aproximadamente 16%) extraída pela metodologia automática de extração de segmentos de rodovias. / Researches on road extraction from digital images are not recent, being the first one from 70's. The methods developed are usually classified into two classes, i.e., semiautomatic and automatic. Concerning this last class, no intervention with the operator is expected. In this context, the road crossing extraction is fundamental, although very few works are found in the relevant literature. The main reason is the great diversity of road crossings, bringing some difficulties to build up a priori knowledge of them. This research proposes a methodology for solving this problem combining road segments extracted from a high – resolution image (pixel < 0.7 m), with lines extracted from a low - resolution image (pixel > 2m) resampled from the original, high – resolution image. The proposed methodology was tested with a set of high – resolution image, showing that it is reliable and efficient. Whenever the road edges were well - defined the method was able to totally extract the road crossings. In addition it provided road networks with completion significantly better (about 16%) than the corresponding ones previously extracted by the automatic road segment extraction method.

Made available in DSpace on 2014-06-11T19:30:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-10-31Bitstream added on 2014-06-13T20:47:04Z : No. of bitstreams: 1 gallis_rba_dr_prud.pdf: 3261376 bytes, checksum: 6967d0b5771ef57a837696cfb04efa2f (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Este trabalho propõe uma nova metodologia para extração de rodovias utilizando imagens aéreas digitais. A inovação baseia-se no algoritmo de Programação dinâmica (PD), que nesta metodologia realiza o processo de otimização no espaço objeto, e não no espaço imagem como as metodologias tradicionais de extração de rodovias por PD. A feição rodovia é extraída no espaço objeto, o qual implica um rigoroso modelo matemático, que é necessário para estabelecer os pontos entre o espaço imagem e objeto. Necessita-se que o operador forneça alguns pontos sementes no espaço imagem para descrever grosseiramente a rodovia, e estes pontos devem ser transformados para o espaço objeto para inicialização do processo de otimização por PD. Esta metodologia pode operar em diferentes modos (modo mono e estéreo), e com diversos tipos de imagens, incluindo imagens multisensores. Este trabalho apresenta detalhes da metodologia mono e estéreo e também os experimentos realizados e os resultados obtidos. / This work proposes a novel road extraction methodology from digital images. The innovation is based on the dynamic programming (DP) algorithm to carry out the optimisation process in the object space, instead of doing it in the image space such as the DP traditional methodologies. Road features are traced in the object space, which implies that a rigorous mathematical model is necessary to be established between image and object space points. It is required that the operator measures a few seed points in the image space to describe sparsely and coarsely the roads, which must be transformed into the object space to make possible the initialisation of the DP optimisation process. Although the methodology can operate in different modes (mono-plotting or stereoplotting), and with several image types, including multisensor images, this work presents details of our single and stereo image methodology, along with the experimental results.

Orientador: Aluir Porfírio Dal Poz / Banca: João Fernando Custódio da Silva / Banca: Daniel Rodrigues dos Santos. / Resumo: Nesta pesquisa é proposta uma metodologia para a orientação semi-automática de imagens CBERS usando rodovias como controle de campo. Baseia-se numa estratégia iterativa envolvendo três etapas. Na primeira etapa um operador identifica na imagem as rodovias de controle de campo e fornece alguns pontos sementes, distribuídos grosseira e esparsamente ao longo ou nas imediações das rodovias. Estes pontos sementes são utilizados pelo algoritmo de otimização de programação dinâmica para extrair as rodovias na imagem. Na segunda etapa são estabelecidas correspondências pontuais entre as rodovias de controle e as correspondentes rodovias extraídas na imagem. Na terceira etapa as correspondências pontuais são utilizadas para orientar a imagem usando a DLT (Direct Linear Transformation). As duas últimas etapas do processo são iteradas até que se verifique a estabilização do processo de orientação. Os resultados experimentais possibilitaram verificar que a metodologia proposta foi eficiente com várias imagens teste. Em todos os casos se verificou a convergência do processo de orientação. Além disso, os parâmetros estimados de orientação possibilitaram o registro de rodovias de verificação com acurácia no nível do pixel ou melhor. / Abstract: In this research is proposed a methodology for semiautomatic CBERS image orientation using roads as ground control. It is based on an iterative strategy involving three steps. In the first step, an operator identifies on the image the ground control roads and supplies along them a few distributed seed points, which could be sparsely and coarsely distributed. These seed points are used by the dynamic programming algorithm for extracting the ground control roads from the image. In the second step, it is established the correspondences between points describing the ground control roads and the corresponding ones extracted from the image. In the last step, the corresponding points are used to orient the CBERS image by using the DLT (Direct Linear Transformation). The two last steps are iterated until the convergence of the orientation process is verified. Experimental results showed that the proposed methodology was efficient with several test images. In all cases the orientation process converged. Moreover, the estimated orientation parameters allowed the registration of check roads with pixel accuracy or better. / Mestre

Orientador: Aluir Porfírio Dal Poz / Resumo: Pesquisas em extração de rodovias em imagens digitais não são recentes, sendo as primeiras da década de 70. Os métodos desenvolvidos normalmente são classificados em duas classes: semi-automático e automático. Esta última classe não prevê a intervenção de um operador. Neste contexto, a extração automática de cruzamentos de rodovias é fundamental, embora muito poucos trabalhos são encontrados na literatura sobre extração de rodovias. A razão principal é a grande diversidade de cruzamentos de rodovias, gerando algumas dificuldades para construir um conhecimento a priori sobre cruzamentos. Esta pesquisa propõe uma metodologia automática para resolver este problema, combinando segmentos de rodovias extraídos de uma imagem de alta resolução (pixel < 0,7 m), com linhas extraídas numa imagem de baixa resolução (pixel > 2m), reamostrada da imagem original de alta resolução. A metodologia proposta foi testada em um conjunto de imagens de alta resolução, mostrando-se confiável e eficiente. Desde que as bordas das rodovias sejam satisfatoriamente definidas, o método se mostrou capaz de extrair totalmente os cruzamentos de rodovias. Além disso, gerou uma significante melhora na malha viária (aproximadamente 16%) extraída pela metodologia automática de extração de segmentos de rodovias. / Abstract: Researches on road extraction from digital images are not recent, being the first one from 70's. The methods developed are usually classified into two classes, i.e., semiautomatic and automatic. Concerning this last class, no intervention with the operator is expected. In this context, the road crossing extraction is fundamental, although very few works are found in the relevant literature. The main reason is the great diversity of road crossings, bringing some difficulties to build up a priori knowledge of them. This research proposes a methodology for solving this problem combining road segments extracted from a high - resolution image (pixel < 0.7 m), with lines extracted from a low - resolution image (pixel > 2m) resampled from the original, high - resolution image. The proposed methodology was tested with a set of high - resolution image, showing that it is reliable and efficient. Whenever the road edges were well - defined the method was able to totally extract the road crossings. In addition it provided road networks with completion significantly better (about 16%) than the corresponding ones previously extracted by the automatic road segment extraction method. / Mestre

Extraction of streets from Google earth imagery is a hot research topic. The main purpose of this paper is to create a method to extract streets information from satellite image automatically. It is exceedingly difficult to achieve, because every road has different characters and there are a lot of noises (e.g. shadow, building, and vehicle) in the image. By using generic color model and the image analysis techniques, we build up the automatic road extraction system. It extracted road successfully from mid-size city image with a very high extraction rate. Some interesting discoveries and unique creative solution are proposed in this paper.

In this thesis, an automatic road extraction algorithm for multi-spectral images is developed. The developed model extracts elongated structures from images by using edge detection, segmentation and clustering techniques. The study also extracts non-road regions like vegetative fields, bare soils and water bodies to obtain more accurate road map. The model is constructed in a modular approach that aims to extract roads with different characteristics. Each module output is combined to create a road score map. The developed algorithm is tested on 8-band WorldView-2 satellite images. It is observed that, the proposed road extraction algorithm yields 47 % precision and 70 % recall. The approach is also tested on the lower spectral resolution images with four-band, RGB and gray level. It is observed that the additional four bands provide an improvement of 12 % for precision and 3 % for recall. Road type analysis is also in the scope of this study. Roads are classified into asphalt, concrete and unpaved using Gaussian Mixture Models. Other linear objects such as railroads and water canals may also be extracted by this process. An algorithm that classifies drive roads and railroads for very high resolution images is also investigated. It is based on the Fourier descriptors that identify the presence of railroad sleepers. Water canals are also extracted in multi-spectral images by using spectral ratios that employ the near infrared bands. Structural properties are used to distinguish water canals from other water bodies in the image.