Special Issue "Point Cloud Processing in Remote Sensing Technology"
Deadline for manuscript submissions: 30 November 2022.
Interests: point cloud processing; laser scanning; spatial indices; efficient processing concepts; least-squares; point cloud orientation and strip adjustment
Interests: computer vision; pattern recognition; machine learning; photogrammetry; remote sensing
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Interests: LiDAR remote sensing; point cloud processing; 3D reconstruction; tree modeling; vegetation structure analysis
Interests: photogrammetry; 3D computer vision; remote sensing; machine learning; deep learning; automated interpretation of imagery and point clouds
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Special Issue in Remote Sensing: Laser Scanning and Point Cloud Processing in Urban Environments
Modern data acquisition with active or passive remote sensing techniques often results in 3D point clouds. While point clouds were long regarded as an intermediate product for deriving 2.5D or 3D models, they are nowadays accepted as a primary data product that plays a central role in a huge variety of applications.
The technological advances and the miniaturisation of remote sensing hardware led to the development of a large number of distinctive devices for capturing 3D point clouds at different scales, resolutions and precisions. For instance, laser scanners, single-camera systems and multi-camera systems (in conjunction with image matching), RGBD cameras, time-of-flight sensors, synthetic aperture radar systems, ground-penetrating radar systems, echo sounding systems, index arms with tactile tip or scanning heads, etc., are used on static (e.g., tripod) or kinematic platforms (e.g., robot, car, boat, UAV, helicopter, airplane, or satellite) to capture objects or scenes of different scale via close-range, mid-range or far-range measurements.
Although the capturing procedure is the starting point for many applications, the processing of 3D point clouds is essential to visualise, enrich, analyse, quantify, evaluate, model, and to understand the measured object or scene. A processing pipeline typically consists of multiple stages, such as point cloud orientation, co-registration, quality control, feature extraction, semantic segmentation and classification, object detection and recognition, change detection, and object modelling. This Special Issue will report cutting-edge methods, algorithms, and data structures of certain stages or comprehensive processing pipelines for specific applications or sensors.
The Special Issue invites authors to submit contributions in (but not limited to) the following topics:
- Point cloud generation and quality analyses for new or improved sensors, such as miniaturised cameras and laser scanners, integrated sensors, Geigermode and single-photon LiDAR systems, UAV-based laser scanning systems, mobile mapping systems, multi-beam echo sounding systems, tomographic synthetic aperture radar systems, etc.;
- Deep learning methods, specific network designs, transfer learning, and data organisation strategies for realising new or improved classification and object detection tasks as required for self-driving cars, indoor navigation, object modelling, etc.;
- Classical semantic segmentation and classification methods are still relevant for many tasks, especially when processing huge point clouds due to the computational burden of deep learning and the lack of a sufficient amount of training data;
- Innovative 2.5D and 3D modelling algorithms, as often used in mobile and corridor mapping, but also for traditional topographic point clouds, such as terrain, surface, building and tree modelling;
- Data fusion of point clouds acquired from different sensors, scales, and accuracies. Today's sensor heads typically combine multiple sensor elements, such as differently oriented cameras (forward, nadir, backward and oblique views), laser scanners with multiple channels or different wavelengths (infrared and green laser diodes), etc.;
- Multi-temporal analyses, which are used, for example, for change detection, updating inventory databases, land slide monitoring, or disaster management;
- Methods and algorithms for interacting with point clouds to visualise, inspect, and highlight specific aspects of the dataset;
- Optimised algorithms, strategies and data structures for efficiently processing huge point clouds.
Dr. Johannes Otepka
Dr. Martin Weinmann
Dr. Di Wang
Prof. Kourosh Khoshelham
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Point cloud generation
- Mobile mapping
- Photogrammetric point clouds
- Dense image matching
- 3D modelling
- Point cloud analysis
- Quality and accuracy estimation
- Feature extraction
- Semantic segmentation
- Supervised and unsupervised machine learning
- Deep learning
- Data fusion
- Change detection