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Record W7064171322

Avaliação da informação planialtimétrica derivada de dados RADARSAT-2 e TerraSAR-X para produção de cartas topográficas na escala 1:50.000

2011· dissertation· pt· W7064171322 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioteca Digital da Memória Científica do INPE (National Institute for Space Research) · 2011
Typedissertation
Languagept
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsScale (ratio)Synthetic aperture radarRadar imagingField (mathematics)Polynomial
DOInot available

Abstract

fetched live from OpenAlex

Apenas 15% do território nacional possuem mapas topográficos na escala 1:50.000 e parte desses mapas está desatualizada. Nos últimos anos, as aplicações cartográficas foram beneficiadas com a disponibilidade de imagens de alta resolução espacial coletadas por radares de abertura sintética (Synthetic Aperture Radar - SAR) orbitais, como o canadense RADARSAT-2 (RST-2) e alemão TerraSAR-X (TSX). Assim, o objetivo desta pesquisa consistiu em avaliar o potencial das imagens RST-2 adquiridas nos modos Ultra-Fine e Fine Quad. Pol., e imagens TSX adquiridas nos modos SpotLight e Stripmap, para o mapeamento topográfico de escala 1:50.000 para duas áreas de estudo: Província Mineral de Carajás (PMC), estado do Pará, e Vale do Rio Curaçá (VRC), estado da Bahia. Dados planialtimétricos coletados em campo com receptores GPS geodésicos foram utilizados como pontos de controle de campo (Ground Control Points - GCPs) para a geração de Modelos Digitais de Superfície (Digital Surface Models DSMs) e ortoimagens, e também como pontos independentes de verificação (Independente Check Points ICPs) para a avaliação da acurácia planialtimétrica dos produtos. Duas modelagens matemáticas foram utilizadas para a geração dos produtos: Toutins 3D Radargrammetric (TR) e Funções Racionais (Rational Functions - RF). Distintas quantidades de GCPs foram utilizadas com ambas as modelagens. A primeira modelagem (TR) foi aplicada para as imagens RST-2 e TSX, enquanto a segunda (RF) foi aplicada apenas para as imagens RST-2, desde que os RPCs (Rational Polynomial Coefficients) são fornecidos exclusivamente com os metadados do SAR canadense. Todos os DSMs gerados na modelagem TR, com 8, 10 e 12 GCPs, atenderam aos requisitos altimétricos do Padrão de Exatidão Cartográfica (PEC) nacional para a escala de mapeamento 1:50.000, Classe A (Erro Padrão < 6,67 m). Por outro lado, todos os DSMs RST-2 gerados na modelagem RF para ambas as áreas de estudo, sem GCPs ou com apenas um GCP, atenderam aos requisitos altimétricos do PEC nacional, com exceção do produto gerado sem GCPs para a PMC. Com relação às ortoimagens RST-2 (Ultra-Fine e Fine Quad. Pol.) e TSX (SpotLight e Stripmap), todos os produtos atenderam aos requisitos planimétricos do PEC nacional para a escala 1:50.000, Classe A (Erro Padrão < 15 m). Para a produção de um mapa topográfico, informações planialtimétrica e temática são necessárias. Assim, a informação planialtimétrica, representada por estradas, rios, curvas de nível, etc., foi derivada dos DSMs e ortoimagens RST-2. Já a informação temática, representada pelo uso e ocupação da terra, foi extraída das imagens RST-2 Fine Quad. Pol. através do classificador de Wishart Freeman-Durden (WFD), específico para imagens SAR polarimétricas. As classificações de WFD foram avaliadas a partir do uso do coeficiente de concordância Kappa, com resultados considerados satisfatório e bom para as áreas da PMC e VRC, respectivamente. Como exemplo, um mapa final foi gerado para a área de estudo do VRC integrando as informações planialtimétrica e temática, extraídas dos dados RST-2. Os resultados da investigação indicam um grande potencial das imagens RADARSAT-2 adquiridas nos modos Ultra-Fine e Fine Quad. Pol., e das imagens TerraSAR-X adquiridas nos modos SpotLight e Stripmap, como uma alternativa viável para suprir a falta de mapeamento topográfico na escala 1:50.000 em grandes áreas do país. ABSTRACT: Only 15% of the Brazilian national territory are topographically mapped at 1:50,000 scale and part of these maps is outdated. In recent years, cartographic applications have been benefited with the availability of high resolution spatial images collected by orbital Synthetic Aperture Radar (SAR), such as the Canadian RADARSAT-2 (RST-2) and the German TerraSAR-X (TSX). The objective of this research was to assess the potential of RST-2 images acquired in Ultra-Fine and Fine Quad. Pol. modes, and TSX images acquired in SpotLight and Stripmap modes, for topographic mapping at 1:50,000 scale for two study areas: Carajás Mineral Province (CMP), Pará state, and Curaçá River Valley, Bahia state. Planialtimetric field data acquired with geodetic GPS receivers were used as ground control points (GCPs) for the Digital Surface Models (DSMs) and orthoimages generations and also as Independent Check Points (ICPs) for the planialtimetric accuracy assessment of the products. Two math modeling were used for the generation of the products: (1) Toutins 3D Radargrammetric (TR) and (1) Rational Functions (RF). Distinct quantities of GCPs were used with both models. The first model (TR) was applied for the RST-2 and TSX images, while the second (RF) was applied only to the RST-2 images, since Rational Polynomial Coefficients (RPCs) are provided with the metadata solely with the Canadian SAR. All the DSMs generated on the TR modeling with 8, 10 and 12 GCPs have met the altimetric requirements for the Brazilian Standard for Cartographic Accuracy (PEC in Portuguese) for 1:50,000 A Class map (Standard Error < 6.67 m). On the other hand, all the RST-2 DSMs generated for the both study areas using RF modeling, with only one GCP or without GCPs, have fulfilled the altimetric PEC requirements, with exception of the product generated without GCPs for the CMP. Regarding the RST-2 (Ultra-Fine and Fine Quad. Pol.) and TSX (SpotLight and Stripmap) orthoimages, all products met the planimetric PEC requirements for the 1:50,000 mapping scale, A Class (Standard Error < 15 m). Aiming at the production of a topographic map, planialtimetric and thematic information are necessary. Thus, planialtimetric information, represented by contour lines, roads, drainages, etc., was derived from the RST-2 DSMs and orthoimages. The thematic information represented by land use and land cover was derived from the RST-2 Fine Quad. Pol. images through the Wishart Freeman-Durden (WFD) classifier, specific for polarimetric SAR images. The WFD classifications were evaluated from the usage of the Kappa agreement coefficient, with results considered satisfactory and good for CMP and CRV areas, respectively. As an example, a final map was generated for the CRV study area integrating the planialtimetric and thematic information extracted from RST-2 data. The results of the investigation indicate a great potential of the RADARSAT-2 images acquired in the Ultra-Fine and Fine. Quad. Pol. images, and the TerraSAR-X images acquired in the SpotLight and Stripmap modes, as a viable alternative to supply the lack of topographic mapping at the 1:50,000 scale in large areas of the country.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.009
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.097
GPT teacher head0.363
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2011
Admission routes1
Has abstractyes

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