Special Issue "Fusion of LiDAR Point Clouds and Optical Images"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 30 April 2017
Prof. Dr. Jixian Zhang
President of National Quality Inspection and Testing Center for Surveying and Mapping Products, full professor of Chinese Academy of Surveying and Mapping (CASM), Beijing 100830, China
Interests: image processing; geographic information systems; digital photogrammetry; pattern recognition and intelligent control
Dr. Xiangguo Lin
Chinese Academy of Surveying and Mapping, No. 28 Lianhuachixi Road, Beijing 100830, China
Interests: road extraction, vehicle extraction, cropland extraction, object-based image analysis, data fusion, information extraction from LiDAR point clouds
Optical image and LiDAR (Light Detection And Ranging) point cloud are two types of major data sources in the fields of photogrammetry and remote sensing, computer vision, pattern recognition, machine learning, etc. However, the two types of data have quite different history. Specifically, the images, often collected by various types of cameras or imaging spectrometers, have a long history. Compared with images, LiDAR point cloud, acquired by the newly rising laser scanning technique, is a new data type.
The advantages of one type of data source over the other have been the topic of studies and discussions during the last two decades. After evaluating both the merits and demerits, some researchers prefer LiDAR point cloud to images. On the other hand, some scholars advocate that image-based-photogrammetry still has a continued role in both industry and scientific research. Moreover, engineering applications suggest that each type of data has its role in service. Recently, more and more researchers conclude that optical imagery and LiDAR point clouds have distinct characteristics that render them preferable in certain applications, and fusion of LiDAR point cloud and image would achieve a better performance in various applications than that can be achieved using a single type of data source. The fusion of LiDAR point cloud and imagery has been performed in various areas, including registration, generation of true orthophotograph, pixel-based image pan-sharpening, classification, target recognition, 3D reconstruction, change detection, forest inventory, etc.
Against this background, this Special Issue will document the methodologies, developments, techniques and applications of “Fusion of LiDAR Point Clouds and Optical Images”. Well-prepared, unpublished submissions that address one or more of the following topics are solicited:
- Generation of digital true orthophotographs
- Land use and land cover classification
- Ground detection
- Road detection
- Building detection
- Vehicle detection
- 3D building reconstruction
- 3D City reconstruction
- 3D reconstruction of cultural heritage
- Change detection
- Forest inventory
- Tree species classification
- Individual tree delineation
- Forest parameters estimation
- Biomass estimation
- Population estimation
Prof. Dr. Jixian Zhang
Dr. Xiangguo Lin
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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 monthly journal published by MDPI.