Search USGSSearch

USGS · 70201352

HRSC on Mars Express: Photogrammetric and cartographic research

Abstract

The High Resolution Stereo Camera (HRSC) on the European spacecraft Mars Express is the first camera on a planetary mission especially designed for photogrammetric and cartographic purposes. Since January 2004 the camera has been taking image data from the Martian surface, characterized by high-resolution, stereo capability and color. These data provide an enormous potential for the generation of 3D surface models, color orthoimages, topographic and thematic maps, and additional products. The image data acquired undergo calibration and systematic processing to orthoimages and 3D data products. Within the international HRSC Science Team the members of the Photogrammetric/Cartographic Working Group are concerned with further refinements in order to achieve highest quality data products. These activities comprise improvements of the exterior orientation of the camera, various approaches to enhance DTM quality, and the generation of maps in the standard scale of 1:200 000 and larger scales as well. The paper reports on these activities and the results achieved so far .

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jeorg Albertz, Maria Attwenger, Janet M. Barrett, Simon Casley, Peter Dorninger, Egon Dorrer, Heinrich Ebner, Stephan Gehrke, Bernd Giese, Klaus Gwinner, Christian Heipke, Elpitha Howington-Kraus, Randolph L. Kirk, Hartmut Lehmann, Helmut Mayer, Jan-Peter Muller, Juergen Oberst, Alexey Ostrovskiy, Joerg Renter, Sergiy Reznik, Ralph George Schmidt, Frank Scholten, Michael Spiegel, Uwe Stilla, Marita Wahlisch, Gerhard Neukum. 2005. HRSC on Mars Express: Photogrammetric and cartographic research. https://doi.org/10.14358/pers.71.10.1153

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Evaluating Three-Dimensional Elevation Program lidar consistency and accuracy at scale using cloud-native, open-source methods

The U.S. Geological Survey three-dimensional elevation program (3DEP) has significantly expanded national lidar coverage, necessitating scalable, reproducible methods for assessing data quality across diverse terrains and acquisition conditions. This study introduces a cloud-native, open-source workflow designed to evaluate the geometric accuracy and consistency of 3DEP lidar data sets at a national scale. Leveraging tools such as the Point Data Abstraction Library, Open3D, and Amazon Web Services infrastructure, the workflow integrates global navigation satellite system‐surveyed ground control points and terrestrial laser scanning data to validate airborne lidar collections. Two case studies demonstrate the application of this process. In Puerto Rico, the process identified vertical biases and inconsistencies in vegetated areas, while in Iowa and Arizona, the process confirmed high vertical accuracy with minimal bias. The results underscore the effectiveness of combining cloud computing with open-source tools to perform large-scale lidar data quality assessments. This process offers a reproducible, efficient solution for nationwide validation of 3DEP data sets, supporting enhanced decision-making in geospatial applications.

Photogrammetric Engineering and Remote Sensing

Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data

Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.

California