3D hyperspectral point cloud generation
- Remote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at theRemote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at the point-cloud scale improves object-based classification and information extraction schemes. This improvements have great potential for numerous land cover mapping and environmental applications.…
| Author details: | Maximilian BrellORCiDGND, Karl SeglGND, Luis GuanterORCiDGND, Bodo BookhagenORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1016/j.isprsjprs.2019.01.022 |
| ISSN: | 0924-2716 |
| ISSN: | 1872-8235 |
| Title of parent work (English): | ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing |
| Subtitle (English): | Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction |
| Publisher: | Elsevier |
| Place of publishing: | Amsterdam |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2019/02/06 |
| Publication year: | 2019 |
| Release date: | 2021/03/24 |
| Tag: | Data fusion; Imaging spectroscopy; In-flight; Laser return intensity; Lidar; Multispectral point cloud; Pixel level; Point cloud segmentation; Preprocessing; Semantic labeling; Sensor fusion; Sharpening; Unmixing |
| Volume: | 149 |
| Number of pages: | 15 |
| First page: | 200 |
| Last Page: | 214 |
| Funding institution: | MILAN Geoservice GmbH; Federal Ministry for Economic Affairs and Energy Germany (BMWi) |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Umweltwissenschaften und Geographie |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
| Peer review: | Referiert |
