4.7 Article

An approach to the radiometric aerotriangulation of photogrammetric images

期刊

出版社

ELSEVIER
DOI: 10.1016/j.isprsjprs.2011.09.011

关键词

Aerial images; Radiometric aerotriangulation; Calibration; Atmospheric correction; Bidirectional effects; Kernel-driven models

资金

  1. National Geographic Institute of Spain (IGN) [UCTR080230]
  2. Tragsatec

向作者/读者索取更多资源

Harnessing the radiometric information provided by photogrammetric flights could be useful in increasing the thematic applications of aerial images. The aim of this paper is to improve relative and absolute homogenization in aerial images by applying atmospheric correction and treatment of bidirectional effects. We propose combining remote sensing methodologies based on radiative transfer models and photogrammetry models, taking into account the three-dimensional geometry of the images (external orientation and Digital Elevation Model). The photogrammetric flight was done with a Z/I Digital Mapping Camera (DMC) with a Ground Sample Distance (GSD) of 45 cm. Spectral field data were acquired by defining radiometric control points in order to apply atmospheric correction models, obtaining calibration parameters from the camera and surface reflectance images. Kernel-driven models were applied to correct the anisotropy caused by the bidirectional reflectance distribution function (BRDF) of surfaces viewed under large observation angles with constant illumination, using the overlapping area between images and the establishment of radiometric tie points. Two case studies were used: 8-bit images with applied Lookup Tables (LUTs) resulting from the conventional photogrammetric workflow for BRDF studies and original 12-bit images (Low Resolution Color, LRC) for the correction of atmospheric and bidirectional effects. The proposed methodology shows promising results in the different phases of the process. The geometric kernel that shows the best performance is the Lidense kernel. The homogenization factor in 8-bit images ranged from 6% to 25% relative to the range of digital numbers (0-255), and from 18% to 35% relative to levels of reflectance (0-100) in the 12-bit images, representing a relative improvement of approximately 1-30%, depending on the band analyzed. (C) 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Environmental Sciences

Monitoring 10-m LST from the Combination MODIS/Sentinel-2, Validation in a High Contrast Semi-Arid Agroecosystem

Juan M. Sanchez, Joan M. Galve, Jose Gonzalez-Piqueras, Ramon Lopez-Urrea, Raquel Niclos, Alfonso Calera

REMOTE SENSING (2020)

Article Environmental Sciences

Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

Amal Chakhar, David Hernandez-Lopez, Rocio Ballesteros, Miguel A. Moreno

Summary: This study assessed the potential of integrating Sentinel-1 and 2A data to perform crop classification and identified the most important input data for accurate results. The best performing scenario integrated VH and VV with NDVI using a cubic support vector machine (SVM) as the classifier.

REMOTE SENSING (2021)

Article Environmental Sciences

Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data

David Hernandez-Lopez, Laura Piedelobo, Miguel A. Moreno, Amal Chakhar, Damian Ortega-Terol, Diego Gonzalez-Aguilera

Summary: Earth Observation imagery is challenging for intermediate users to access and utilize efficiently. This paper introduces a new approach to integrate imagery from two public optical satellite missions, Landsat 8 (L8) and Sentinel 2 (S2), using a Local Nested Grid (LNG) design. The LNG plays a crucial role in the development of new products in the European EO downstream sector, potentially leading to pre-operational downstream services.

REMOTE SENSING (2021)

Article Biochemical Research Methods

Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis

Lloyd A. Courtenay, Diego Gonzalez-Aguilera, Susana Laguela, Susana del Pozo, Camilo Ruiz-Mendez, Ines Barbero-Garcia, Concepcion Roman-Curto, Javier Canueto, Carlos Santos-Duran, Maria Esther Cardenoso-Alvarez, Monica Roncero-Riesco, David Hernandez-Lopez, Diego Guerrero-Sevilla, Pablo Rodriguez-Gonzalvez

Summary: Non-Melanoma skin cancer is a common type of cancer, and early detection is important for successful treatment. Hyperspectral imaging shows promise for non-invasive inspection of skin lesions, with optimal wavelength ranges between 573.45 and 779.88 nm for distinguishing healthy and unhealthy skin, and between 429.16 and 520.17 nm for differentiating cancer types.

BIOMEDICAL OPTICS EXPRESS (2021)

Article Environmental Sciences

Monitoring Crop Evapotranspiration and Transpiration/Evaporation Partitioning in a Drip-Irrigated Young Almond Orchard Applying a Two-Source Surface Energy Balance Model

Juan M. Sanchez, Llanos Simon, Jose Gonzalez-Piqueras, Francisco Montoya, Ramon Lopez-Urrea

Summary: This study conducted over three consecutive growing seasons in a drip-irrigated young almond orchard utilized a Simplified Two-Source Energy Balance (STSEB) model to quantify water use, showing the potential to predict water use in almond orchards by monitoring biophysical parameters such as crop coefficients and vegetation fractional cover.
Article Chemistry, Multidisciplinary

Comparison of Satellite and Drone-Based Images at Two Spatial Scales to Evaluate Vegetation Regeneration after Post-Fire Treatments in a Mediterranean Forest

Jose Luis Martinez, Manuel Esteban Lucas-Borja, Pedro Antonio Plaza-Alvarez, Pietro Denisi, Miguel Angel Moreno, David Hernandez, Javier Gonzalez-Romero, Demetrio Antonio Zema

Summary: The use of drones and satellite imagery allows for a quicker assessment of changes in vegetation cover on burned lands, yet different survey methods may yield varying results.

APPLIED SCIENCES-BASEL (2021)

Article Environmental Sciences

Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information

Amal Chakhar, David Hernandez-Lopez, Rocio Ballesteros, Miguel A. Moreno

Summary: The study utilized scattering models and Neural Networks to estimate surface soil moisture in a semi-arid environment, and used UAVs to acquire digital surface models and soil parameters, demonstrating the effectiveness of the method in early crop growth stages.

REMOTE SENSING (2021)

Article Environmental Sciences

Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison

Joan M. Galve, Juan M. Sanchez, Vicente Garcia-Santos, Jose Gonzalez-Piqueras, Alfonso Calera, Julio Villodre

Summary: This study evaluates the performance of several algorithms for estimating land surface temperature (LST) from Landsat 8/TIRS under high-contrast semiarid agroecosystem conditions. A simplified single band atmospheric correction method was proposed and showed potential for LST estimation, while also setting the uncertainty for LST estimates in high-contrast semiarid agroecosystems.

REMOTE SENSING (2022)

Article Medicine, General & Internal

Deep Convolutional Neural Support Vector Machines for the Classification of Basal Cell Carcinoma Hyperspectral Signatures

Lloyd A. Courtenay, Diego Gonzalez-Aguilera, Susana Laguela, Susana Del Pozo, Camilo Ruiz, Ines Barbero-Garcia, Concepcion Roman-Curto, Javier Canueto, Carlos Santos-Duran, Maria Esther Cardenoso-Alvarez, Monica Roncero-Riesco, David Hernandez-Lopez, Diego Guerrero-Sevilla, Pablo Rodriguez-Gonzalvez

Summary: Non-melanoma skin cancer, particularly basal cell carcinoma, is a common type of cancer. Early detection is vital, and the combination of multispectral imaging and artificial intelligence offers a non-invasive method for detection and classification with high accuracy.

JOURNAL OF CLINICAL MEDICINE (2022)

Article Agronomy

Is the Subsurface Drip the Most Sustainable Irrigation System for Almond Orchards in Water-Scarce Areas?

Francisco Montoya, Juan M. Sanchez, Jose Gonzalez-Piqueras, Ramon Lopez-Urrea

Summary: A study was conducted to investigate the effect of two drip irrigation systems on almond crop growth. The results showed that the subsurface drip irrigation (SDI) system is a suitable strategy for irrigating almond crops, reducing water consumption and increasing irrigation water productivity.

AGRONOMY-BASEL (2022)

Article Construction & Building Technology

Road marking degradation analysis using 3D point cloud data acquired with a low-cost Mobile Mapping System

Mario Soilan, Diego Gonzalez-Aguilera, Ana Del-Campo-Sanchez, David Hernandez-Lopez, Susana Del Pozo

Summary: This study presents a method for analyzing and estimating the degradation level of road markings, as well as providing decision support for preventive and corrective maintenance. The validation on a case study road section demonstrates the method's capability of offering good qualitative visualizations and accurately detecting areas with high degradation.

AUTOMATION IN CONSTRUCTION (2022)

Article Environmental Sciences

Convolutional Neural Networks for Agricultural Land Use Classification from Sentinel-2 Image Time Series

Alejandro-Martin Simon Sanchez, Jose Gonzalez-Piqueras, Luis de la Ossa, Alfonso Calera

Summary: Land use classification (LUC) is the process of providing information on land cover and the types of human activity involved in land use. This study uses multispectral reflectance Sentinel-2 images to perform agricultural LUC, achieving high accuracy by arranging pixel information as 2D yearly fingerprints and utilizing CNN for modeling and capturing multispectral temporal patterns. This operational tool shows promising potential for monitoring crops and water use over large areas.

REMOTE SENSING (2022)

Article Environmental Sciences

Optimized Software Tools to Generate Large Spatio-Temporal Data Using the Datacubes Concept: Application to Crop Classification in Cap Bon, Tunisia

Amal Chakhar, David Hernandez-Lopez, Rim Zitouna-Chebbi, Imen Mahjoub, Rocio Ballesteros, Miguel A. Moreno

Summary: In the context of a changing climate, monitoring agricultural systems is increasingly important. This study proposes an innovative approach of remote sensing data management for crop classification, particularly in a complex landscape such as Cap Bon in Tunisia. The results highlight the importance of optical data in providing acceptable classification performance.

REMOTE SENSING (2022)

Article Agronomy

Determining grapevine water use under different sustainable agronomic practices using METRIC-UAV surface energy balance model

J. M. Ramirez-Cuesta, D. S. Intrigliolo, I. J. Lorite, M. A. Moreno, D. Vanella, R. Ballesteros, D. Herandez-Lopez, I. Buesa

Summary: Due to climate change and the scarcity of natural resources, it is necessary to develop sustainable intensification strategies to optimize water use in vineyards. In this study, water regime, fertilization, and soil management practices were evaluated using the METRIC model and unmanned aerial vehicle imagery. The application of mulching was found to significantly reduce inter-row soil evaporation and increase crop-line evapotranspiration. Deficit irrigation and fully irrigated strategies both increased vine water use and yield, leading to improvements in water use efficiency.

AGRICULTURAL WATER MANAGEMENT (2023)

Article Remote Sensing

SunMap: Towards Unattended Maintenance of Photovoltaic Plants Using Drone Photogrammetry

David Hernandez-Lopez, Esteban Ruiz de Ona, Miguel A. Moreno, Diego Gonzalez-Aguilera

Summary: Global interest in renewable energy resources, such as solar energy, has led to the development of new approaches for inspecting and monitoring PV plants. This paper presents a drone photogrammetry approach using RGB and IRT images to detect hot spots in PV plants. The methodology incorporates advances in photogrammetry and computer vision, resulting in an in-house software application called SunMap that provides automatic and accurate detection of hot spots and generates high-quality cartographic products.

DRONES (2023)

暂无数据