EXPERIMENTAL ASSESSMENT OF TECHNIQUES FOR FISHEYE CAMERA CALIBRATION

Fisheye lens cameras enable to increase the Field of View (FOV), and consequently they have been largely used in several applications like robotics. The use of this type of cameras in close-range Photogrammetry for high accuracy applications, requires rigorous calibration. The main aim of this work is to present the calibration results of a Fuji Finepix S3PRO camera with Samyang 8mm fisheye lens using rigorous mathematical models. Mathematical models based on Perspective, Stereo-graphic, Equi-distant, Orthogonal and Equi-solid-angle projections were implemented and used in the experiments. The fisheye lenses are generally designed following one of the last four models, and Bower-Samyang 8mm lens is based on Stereo-graphic projection. These models were used in combination with symmetric radial, decentering and affinity distortion models. Experiments were performed to verify which set of IOPs (Interior Orientation Parameters) presented better results to describe the camera inner geometry. Collinearity mathematical model, which is based on perspective projection, presented the less accurate results, which was expected because fisheye lenses are not designed following the perspective projection. Stereo-graphic, Equi-distant, Orthogonal and Equi-solid-angle projections presented similar results even considering that Bower-Samyang fisheye lens was built based on Stereo-graphic projection. The experimental results also demonstrated a small correlation between IOPs and EOPs (Exterior Orientation Parameters) for Bower-Samyang lens

ANÁLISE DOS CONTINGENTES POPULACIONAL E HABITACIONAL DA CIDADE DE AQUIDAUANA-MS: ATUALIZAÇÃO E PERSPECTIVA FUTURA.

O presente artigo analisa o aumento dos contingentes populacional e habitacional na área urbana do Município de Aquidauana – MS, por meio de dados bibliográficos e estatísticos e de sistemas de informação geográfica (SIG). A análise abarca pesquisas e dados censitários, referentes ao período de 1970 e 2010,  abordando as possibilidades de direcionamento e as tendências de agrupamento das habitações dentro do perímetro urbano de Aquidauana. Tanto a população, quanto os domicílios urbanos de Aquidauana, aumentaram a uma taxa relativamente constante ao longo das ultimas décadas, mas com significativa concentração de novos imóveis em algumas áreas da cidade

ANÁLISE DOS CONTINGENTES POPULACIONAL E HABITACIONAL DA CIDADE DE AQUIDAUANA-MS: ATUALIZAÇÃO E PERSPECTIVA FUTURA.

O presente artigo analisa o aumento dos contingentes populacional e habitacional na área urbana do Município de Aquidauana – MS, por meio de dados bibliográficos e estatísticos e de sistemas de informação geográfica (SIG). A análise abarca pesquisas e dados censitários, referentes ao período de 1970 e 2010,  abordando as possibilidades de direcionamento e as tendências de agrupamento das habitações dentro do perímetro urbano de Aquidauana. Tanto a população, quanto os domicílios urbanos de Aquidauana, aumentaram a uma taxa relativamente constante ao longo das ultimas décadas, mas com significativa concentração de novos imóveis em algumas áreas da cidade

Análise da Acurácia Altimétrica do Modelo Digital de Superfície ALOS AW3D30 para o Mato Grosso do Sul

O Mato Grosso do Sul apresenta riquezas ambientais a se destacar. O Pantanal, como exemplo, é a maior área alagável do planeta e é considerado patrimônio natural da humanidade pela Unesco. Os MDS (Modelos Digitais de Superfície) são utilizados para diversas aplicações, no que tange a representação do terreno. Existem diversos MDS que são disponibilizados gratuitamente. Os dados do ALOS AW3D30 foram disponibilizados gratuitamente em maio de 2015, e são escassos os estudos visando sua validação. Nesse sentido, o objetivo do trabalho foi avaliar a acurácia altimétrica do MDS ALOS AW3D30, para o Estado do Mato Grosso do Sul. Utilizamos dados de 167 estações SAT-GPS do IBGE como pontos de referência altimétrica. Para fins comparativos, utilizou-se o SRTM (30 m) versão 3, pois consiste em um dos MDS mais conhecidos, o qual apresentou resultados mais acurados em estudo anterior. Para desenvolver a análise dos MDS, aplicou-se o Padrão de Exatidão Cartográfica dos Produtos Cartográficos Digitais – PEC-PCD. Verificou-se que o MDS ALOS AW3D30 apresenta RMSE (Root Mean Square Error) menor que o SRTM, indicando que para os dados avaliados apresenta maior acurácia. Para o estado do Mato Grosso do Sul, conclui-se que a acurácia altimétrica dos dados do ALOS AW3D30, assim como aqueles do SRTM, têm acurácia compatível com a escala 1:50.000, classe A, PEC-PCD

Geometrical Correction of CBERS-4/PAN Images with Generalized Models Using as Reference National System of Land Management Data

The orbital images have been widely used in several applications in the Earth observation context, which require different levels of detail and positional accuracy. The China-Brazil Earth Resources Satellite Program (CBERS) program was originated from a partnership between Brazil and China in the technical-scientific spatial sector. The CBERS-4 satellite is the fifth satellite of the CBERS Program and contains the PAN sensor, which collects panchromatic images with spatial resolution element (GSD - Ground Sample Distance) of 5 m. The researches related to the analysis of positional reliability and geometric correction of CBERS-4 images are still limited. Previous studies with CBERS-4 PAN images with different levels of processing indicate significant positional displacements of the georeferenced images, which are available by INPE (National Institute of Space Research). The positional displacements are incompatible with its GSD. The objective of this work was to investigate the application of generalized mathematical models in the geometric correction of CBERS-4 PAN images using rural properties limits of INCRA (Instituto Nacional de Colonização e Reforma Agrária) as control points. These limits are available for properties all over Brazil, which makes it possible to replicate the work at the national level. Images with different levels of previous correction (levels 1 and 2) were considered. Level 1 images are derived only from the application of radiometric calibration procedures, while level 2 images are level 1 images geometrically corrected from satellite orbital data information. In the experiments were considered 3 (three) images at level 1 and 1 (one) image at level 2. The following generalized models were adopted: Polynomials of order 1, 2 and 3; Projective and; Thin-plate spline (TPS). Generalized models have the advantage of not requiring knowledge of the system acquisition parameters, such as focal length, sensor size, among others. However, the generalized models require a significant amount of control points with uniform distribution throughout the image. For the geometric correction process were used different configurations of control points (30, 25, 20, 15 and 10) coinciding with the georeferenced rural properties in the Mato Grosso do Sul state, which presents accuracy higher than 50 cm. The geometric correction validation was performed from the RMSE (Root Mean Square Error) at checkpoints. The polynomial transformation of order 1 presented high values (higher than 10 GSD - 50 meters) of RSME when compared to the other mathematical models, even considering 30 control points. The polynomial model of order 2 presented consistent behavior higher than the other models. Even when considering only 10 GCP presented RMSE between 1 and 2 GSD. In this model there is no significant improvement in the results, even increasing the number of control points. In the other models (TPS, Projection and Polynomial of order 3), there was a significant increase in RMSE when the number of points was reduced. The images used in this work cover part of the Mato Grosso do Sul state, which encompasses the most part of the Pantanal, considered a natural patrimony of humanity. Therefore, these orbital images contribute to the mapping and monitoring of their natural resources and, consequently, the protection of this patrimon

The Potential of Visual ChatGPT For Remote Sensing

Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model is Visual ChatGPT, which combines ChatGPT's LLM capabilities with visual computation to enable effective image analysis. The model's ability to process images based on textual inputs can revolutionize diverse fields. However, its application in the remote sensing domain remains unexplored. This is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model's limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field

Variação da Cobertura do Solo no Pantanal de 2000 a 2015 por Sensoriamento Remoto com Software e Dados Gratuitos

Due to the great environmental importance and the advance of the agro pastoralist activities in Pantanal, the aim of the current study is to analyze the modifications that occurred in the land cover, considered one of the richest areas in biodiversity of the planet. The work is justified on the importance of preserving the Pantanal, since it plays a very important role in the maintenance of such biodiversity. As the livestock is the basis of the local economy, and knowing that it is the activity that most promote deforestation, one should intensify the monitoring to ensure the conservation and predict potential impacts on forest vegetation. Thus, for this work we used TERRA satellite images, MODIS sensor, which are available for free, and also NDVI data (Normalized Difference Vegetation Index), MOD13Q1 product, from the same sensor of two distinct years - 2000 and 2015. Such data were analyzed on QGIS 2.8 (QGIS Development Team, 2015) freely available, where through false color multitemporal reclassification, the change in the land cover was verified between the two years. The results show a reduction of 28.8% of the forest vegetation and an increasing of 11.6% of undergrowth (fields or pastures). It is interesting to highlight that it is possible to apply the methodology used in this work elsewhere without financial cost, since there was no need to purchase the images and software.Devido à grande importância ambiental e diante do avanço das atividades agropastoris no Pantanal, o presente estudo tem como objetivo analisar as modificações ocorridas na cobertura do solo na região, considerada uma das áreas mais ricas em biodiversidade do planeta. O trabalho justifica-se na relevância da preservação do Pantanal, já que este desempenha papel importantíssimo na manutenção de tal biodiversidade. Sendo a pecuária a base da economia local, e sabendo que é a atividade que mais promove o desmatamento, deve-se redobrar o monitoramento para garantir a conservação e prever possíveis impactos na vegetação. Assim, para a realização deste trabalho utilizaram-se imagens do satélite TERRA, sensor MODIS, as quais estão disponíveis de forma gratuita na rede, utilizando-se também dados de NDVI (índice de vegetação da diferença normalizada), produto MOD13Q1, do mesmo sensor e também gratuito, de dois anos distintos -- 2000 e 2015. Tais dados foram analisados no SIG (sistemas de informações geográficas) livre e gratuito QGIS versão 2.8 (QGIS Development Team, 2015), onde através de composições falsa-cor multitemporais e de reclassificação verificou-se a mudança na cobertura do solo do Pantanal entre os dois anos. O resultado apresentou a redução de 28,8% de vegetação arbóreo-arbustiva e o aumento de 11,6% de vegetação rasteira (campos ou pastagens). É interessante destacar que a metodologia utilizada neste trabalho é passível de ser aplicada em outros locais sem custo, visto que não houve a necessidade de gastos com compra de imagens e softwares