Made available in DSpace on 2014-06-11T19:23:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2001-11-26Bitstream added on 2014-06-13T20:50:28Z : No. of bitstreams: 1 ishikawa_mi_me_prud.pdf: 658123 bytes, checksum: e1d7f4d23437dc8bb19a8805ed344335 (MD5) / O grande avanço tecnológico desta década, na área de Sensoriamento Remoto, pode ser percebido quando são observadas as grandes mudanças nas características dos sistemas orbitais mais tradicionais, bem como da nova geração de sistemas sensores desenvolvidos com o intuito de auxiliar, cada vez mais, as tarefas de identificação de alvos na superfície terrestre, devido à grande melhoria na resolução espacial. Produtos orbitais de alta resolução, com grau de detalhamento em torno do metro, permitem um melhor aproveitamento das imagens em aplicações cartográficas. O mercado de mapeamento urbano atualmente é ainda quase inteiramente baseado em fotografias aéreas. Porém, o Sensoriamento Remoto orbital vem passando por uma grande evolução tecnológica desde o final de 1999, quando foi lançado pela empresa norte-americana Space Imaging o satélite IKONOS. Este satélite possui sensores capazes de gerar imagens com lmetro de resolução espacial no modo pancromático e 4 metros no modo multiespectral. Estas imagens permitem o mapeamento da cobertura e uso do solo de maneira detalhada e continuada, desde que sejam usados métodos e/ou técnicas apropriadas. Este trabalho teve como objetivo fazer um estudo do potencial de uso das imagens geradas pelo satélite IKONOS, produto Geo, no que diz respeito a escala máxima de utilização em aplicações cartográficas. O procedimento para verificar a exatidão cartográfica baseou-se na análise estatística das discrepâncias entre as coordenadas de pontos no terreno, obtidas através do GPS, e as coordenadas dos pontos homólogos extraídas da imagem IKONOS, através da análise da existência de tendências e da precisão. Como resultado final, chegou-se a conclusão que a imagem IKONOS/Geo utilizada é adequada a escala 1:50000 e menores. / The huge technological advancement that occurred in this decade, in the field of Remote Sensing, can be well perceived when we observe the great changes that occurred in the characteristics of the more traditional orbital systems, as well as of those which belong to the new generation of sensor systems developed with the aim of helping, more and more, the tasks of identification of targets on the Earth surface, due to the improvement on the spatial resolution. Orbital products of high resolution with the possibility of showing details of about one meter in size allow a better employment of imagery in cartographic applications. The urban mapping market is nowadays almost totally based on aerial photography. However, the orbital Remote Sensing is getting through a immense technological evolution since the end of 1999, when the satellite IKONOS was launched by a north American company called Space Imaging. This satellite has sensors capable of generating images with 1 meter resolution in the panchromatic mode and 4 meter resolution in the multispectral mode. These imagery allow mapping the land cover and use in a detailed and continuous manner, providing the appropriate methods and/or techniques are used. This dissertation aimed at studying the potential use of such imagery obtained by IKONOS satellite, Geo Product, specially with respect to the maximum scale of employment for cartographic applications. The approach for the checking the cartographic accuracy was based upon the statistical analysis of discrepancies between the coordinates on the ground, obtained by the use of GPS, and the coordinates of homologue points extracted from the IKONOS imagery, through the analysis of existence of trend and also by the analysis of precision. As a final result, it has been found that the IKONOS/Geo imagery is useful for mapping at 1:50.000 and smaller scales.

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.

Orientador: Erivaldo Antonio da Silva / Resumo: O grande avanço tecnológico desta década, na área de Sensoriamento Remoto, pode ser percebido quando são observadas as grandes mudanças nas características dos sistemas orbitais mais tradicionais, bem como da nova geração de sistemas sensores desenvolvidos com o intuito de auxiliar, cada vez mais, as tarefas de identificação de alvos na superfície terrestre, devido à grande melhoria na resolução espacial. Produtos orbitais de alta resolução, com grau de detalhamento em torno do metro, permitem um melhor aproveitamento das imagens em aplicações cartográficas. O mercado de mapeamento urbano atualmente é ainda quase inteiramente baseado em fotografias aéreas. Porém, o Sensoriamento Remoto orbital vem passando por uma grande evolução tecnológica desde o final de 1999, quando foi lançado pela empresa norte-americana Space Imaging o satélite IKONOS. Este satélite possui sensores capazes de gerar imagens com lmetro de resolução espacial no modo pancromático e 4 metros no modo multiespectral. Estas imagens permitem o mapeamento da cobertura e uso do solo de maneira detalhada e continuada, desde que sejam usados métodos e/ou técnicas apropriadas. Este trabalho teve como objetivo fazer um estudo do potencial de uso das imagens geradas pelo satélite IKONOS, produto Geo, no que diz respeito a escala máxima de utilização em aplicações cartográficas. O procedimento para verificar a exatidão cartográfica baseou-se na análise estatística das discrepâncias entre as coordenadas de pontos no terreno, obtidas através do GPS, e as coordenadas dos pontos homólogos extraídas da imagem IKONOS, através da análise da existência de tendências e da precisão. Como resultado final, chegou-se a conclusão que a imagem IKONOS/Geo utilizada é adequada a escala 1:50000 e menores. / Abstract: The huge technological advancement that occurred in this decade, in the field of Remote Sensing, can be well perceived when we observe the great changes that occurred in the characteristics of the more traditional orbital systems, as well as of those which belong to the new generation of sensor systems developed with the aim of helping, more and more, the tasks of identification of targets on the Earth surface, due to the improvement on the spatial resolution. Orbital products of high resolution with the possibility of showing details of about one meter in size allow a better employment of imagery in cartographic applications. The urban mapping market is nowadays almost totally based on aerial photography. However, the orbital Remote Sensing is getting through a immense technological evolution since the end of 1999, when the satellite IKONOS was launched by a north American company called Space Imaging. This satellite has sensors capable of generating images with 1 meter resolution in the panchromatic mode and 4 meter resolution in the multispectral mode. These imagery allow mapping the land cover and use in a detailed and continuous manner, providing the appropriate methods and/or techniques are used. This dissertation aimed at studying the potential use of such imagery obtained by IKONOS satellite, Geo Product, specially with respect to the maximum scale of employment for cartographic applications. The approach for the checking the cartographic accuracy was based upon the statistical analysis of discrepancies between the coordinates on the ground, obtained by the use of GPS, and the coordinates of homologue points extracted from the IKONOS imagery, through the analysis of existence of trend and also by the analysis of precision. As a final result, it has been found that the IKONOS/Geo imagery is useful for mapping at 1:50.000 and smaller scales. / Mestre

This master thesis deals with a classification technique for an automatic detection of different land cover types from combination of high resolution imagery and LiDAR data sets. The main aim is to introduce additional post-processing method to commonly accessible quality data sets which can replace traditional mapping techniques for certain type of applications. Classification is the process of dividing the image into land cover categories which helps with continuous and up-to-date monitoring management. Nowadays, with all the technologies and software available, it is possible to replace traditional monitoring methods with more automated processes to generate accurate and cost-effective results. This project uses object-oriented image analysis (OBIA) to classify available data sets into five main land cover classes. The automate classification rule set providing overall accuracy of 88% of correctly classified land cover types was developed and evaluated in this research. Further, the transferability of developed approach was tested upon the same type of data sets within different study area with similar success – overall accuracy was 87%. Also the limitations found during the investigation procedure are discussed and brief further approach in this field is outlined.

Classification of road network from airborne laser scanning data and from remote sensing images with high resolution Abstract Object classification of land cover is currently one of the methods of remote Earth exploration. Road network classification only is unique because it is covered with anthropogenic material and has different characteristics than other elements of the landscape. This work deals with the possibility of using a combination of data from airborne laser scanning and high resolution optical data for detection of the road network in the specific area. The premise is that the use of two different types of data could provide better results, because airborne laser scanning data provide very precise information about the position and height of the point, while satellite data of very high resolution represent the real landscape. Searching for suitable features and classification rules for unambiguous determination of the road network is one of the objectives of the work. Segmentation parameters will also be important for object classification. Another objective is to verify the transferability of classification schemes into the other scene. The results should present a response on whether a procedure can be applied over a different location and also that the use of two types of data can bring...

Submitted by Samara Calçado de Azevedo (samara_calcado@hotmail.com) on 2018-09-17T13:15:08Z No. of bitstreams: 1 Azevedo_SC.pdf: 8234866 bytes, checksum: 4f6512aa127d3ab1d52e793725f3a75a (MD5) / Approved for entry into archive by Claudia Adriana Spindola null (claudia@fct.unesp.br) on 2018-09-17T13:46:15Z (GMT) No. of bitstreams: 1 azevedo_sc_dr_prud.pdf: 7732297 bytes, checksum: bebf4345bd7542863867084e462c9ff9 (MD5) / Made available in DSpace on 2018-09-17T13:46:15Z (GMT). No. of bitstreams: 1 azevedo_sc_dr_prud.pdf: 7732297 bytes, checksum: bebf4345bd7542863867084e462c9ff9 (MD5) Previous issue date: 2018-08-30 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Imagens orbitais com alta resolução espacial abriram uma nova era na extração de informações, especialmente em ambientes urbanos, em que valiosas informações sobre as superfícies tornaram-se disponíveis e de forma detalhada. No entanto, a obtenção de informação em áreas urbanas complexas pode ser comprometida pela presença de sombras, que chegam a ocupar uma parte significativa da imagem, causando sérias interferências na sua análise. A remoção de sombras é, portanto, um tema importante e ainda não resolvido devido à dificuldade da tarefa, sendo necessária na etapa de pré-processamento de diversas aplicações. Este trabalho propõe o desenvolvimento de uma nova abordagem automática para a restauração de áreas de sombras em imagens de satélite multiespectrais com alta resolução espacial. A abordagem se divide em três etapas principais e sequenciais, sendo a primeira o pré-processamento, que consiste na conversão das imagens para reflectância aparente e na fusão das bandas para a geração de índices espectrais. Na segunda etapa, a detecção das áreas de sombras é realizada pela combinação do top-hat por fechamento com a injunção de um parâmetro de área, calculado em função do índice de sombras NSDVI (Normalized Saturation-Value Difference Index). As regiões de sombras são refinadas para a geração da máscara de sombras, a qual é utilizada na terceira etapa, como guia na restauração pelo inpainting automático. A estratégia de restauração pelo inpainting híbrido, adaptado para o contexto multiespectral, combina a difusão anisotrópica, ordem de preenchimento definida a partir de uma imagem cartoon e equação de transporte, e o preenchimento baseado em blocos por amostragem local. A avaliação experimental do método proposto foi realizada em um conjunto de 215 imagens, fruto do recorte de duas cenas dos satélites WorldView-2 (WV-2) e Pléiades-1B (PL-1B) sobre a área urbana de São Paulo. Os resultados mostraram uma boa performance da detecção de sombras pelo método proposto, alcançando uma acurácia global média de 94,20% e de 90,84%, para as imagens WV-2 e PL-1B, respectivamente, sendo inclusive superior, quando comparado com outros dois métodos propostos na literatura. A remoção dos efeitos das sombras obtido pelo inpainting, proporcionou uma restauração satisfatória e coerente de feições como vias e telhados, que ficaram melhor discerníveis pelo inpainting. Além disso, quando quantidades substanciais de áreas não contamindas por sombras estão disponíveis, o inpainting aumentou as áreas úteis da imagem mais do que a restauração baseada em outras técnicas de transformação de intensidades, como o matching de histogramas. Assim, frente à necessidade de informações extraídas de imagens multiespectrais, espera-se que o método possa contribuir com a detecção e remoção automática dos efeitos de sombras em imagens multiespectrais com alta resolução espacial de áreas urbanas. / High resolution satellite images has played an important role in information extraction especially in urban areas, since valuable information and higher level of details from surface become available with these images. Nevertheless, the task of extracting information from complex urban environment can be hampered by shadows, which can occupy a significant part into the image and, thus negatively affecting the image analysis. Therefore, due to the complexity of the problem, shadow removal is a crucial as one of the first pre-processing steps to enhance the performance of many subsequent steps and applications. The main goal of this thesis is to present a new automatic method for shadow removal in high spatial resolution satellite multispectral images. The proposed method comprises three main steps: the first one is the pre-processing, which includesthe conversion of the target image to the top of atmosphere (TOA) reflectance and the image pansherpening to spectral indices generation. Secondly, a shadow pixels candidates’ identification is performed, combining black-top-hat (BTH) transformation with area injunction driven by the normalized saturation-value difference index (NSDVI) mask. The obtained output is a shadow mask, which is used to properly guide our automatic inpainting-inspired strategy in the restoration step. In the third step, a hybrid inpainting-based strategy specifically adapted for the multispectral imagery context is applied to recover shadow areas, which unifies anisotropic diffusion, filling-in priority based on cartoon image representations, transport equation and block-based pixel replication using local dynamic sampling. The performance of our approach has been evaluated by taking 215 subset images from WorldView-2 (WV-2) and Pléiades-1B (PL-1B) that encompasses the city of São Paulo. The method achieves an overall accuracy on shadow detection up to 94.2%, for WV-2, and 90.84%, for PL-1B. The comparative results indicate that the proposed method outperforms two existing state-of-the-art methods. Shadow effects are mitigated by local inpainting method in which the satisfactory outputs demonstrate good coherence for highway and building rooftops recovery. Moreover, when largely non-contaminated areas are available in the image, the proposed approach improves significantly more areas of the image than the intensity-based transformation techniques, such as the histogram matching method. Once only multispectral imagery is required as input data, the approach can be suitable to support other remote sensing applications as well. / 2013/25257-4

Indiana University-Purdue University Indianapolis (IUPUI) / Wetland habitats are characterized by periodic inundation and saturation by water creating anaerobic conditions that generate hydric soils and support hydrophytic vegetation. Wetland habitats provide important ecological functions including breeding grounds for fish, other wildlife, water purification, reduction in flooding, species diversity, recreation, food production, aesthetic value, and transformation of nutrients (Tiner, 1999). The multiple benefits of wetlands make them an important resource to monitor. A literature review suggests a combination of geospatial variables and methods should be tested for appropriateness in wetland delineation within local settings. Advancements in geospatial data technology and ease of accessing new, higher resolution geospatial data make study at local levels easier and more feasible (Barrette et al, 2000). The purpose of the current study is to evaluate new sources of geospatial data as potential variables to improve wetland identification and delineation. High resolution multispectral digital imagery, topographic data, and soils information are used to derive and evaluate independent variables. Regression analysis was used to analyze the data.