AN IMPROVED PROGRESSIVE MORPHOLOGICAL FILTERING ALGORITHM BASED ON SPATIALLY-DISTRIBUTED SLOPE VALUE OVER TROPICAL VEGETATED REGIONS

2015 ◽  
Vol 77 (26) ◽  
Author(s):  
Zamri Ismail ◽  
Muhammad Zulkarnain Abdul Rahman ◽  
Mohd Radhie Mohd Salleh ◽  
Ibrahim Busu ◽  
Shahabuddin Amerudin ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Entire Area ◽  
Filtering Algorithm ◽  
Morphological Filtering ◽  
Spatially Distributed ◽  
Elevation Data ◽  
Map Generation ◽  
Ground Point ◽  
Constant Slope ◽  
Airborne Lidar Data

This paper presents a thorough investigation on integrating slope map on the existing progressive morphological filtering algorithm for ground point’s extraction. The existing filtering algorithm employs constant slope value for the entire area. The slope map is either generated from field collected elevation data or ground point obtained from initial filtering of airborne LiDAR data. The filtering process has been performed with recursive mode and it stops after the results of the filtering does not show any improvement and the DTM error larger than the previous iteration. The results show that both data used for slope map generation have decreasing pattern of DTM error with increasing in filtering iteration. The spatially-distributed slope map has significantly improved the quality of the DTM compared to the results of filtering based on a constant slope value

Automatic morphological filtering algorithm for airborne lidar data in urban areas

2019 ◽  
Vol 58 (4) ◽  
pp. 1164 ◽  
Author(s):  
Zhenyang Hui ◽  
Leyang Wang ◽  
Yao Yevenyo Ziggah ◽  
Shangshu Cai ◽  
Yuanping Xia
Keyword(s):  
Urban Areas ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Filtering Algorithm ◽  
Morphological Filtering ◽  
Airborne Lidar Data

scholarly journals Validation of TanDEM-X elevation data for a forested karst area in Slovakia (Central Europe)

2018 ◽  
Author(s):  
Peter Bandura ◽  
Michal Gallay
Keyword(s):  
Multiple Scales ◽  
Space Mission ◽  
Airborne Lidar ◽  
Karst Area ◽  
Lidar Data ◽  
Reference Dataset ◽  
Landform Classification ◽  
Elevation Data ◽  
Airborne Lidar Data ◽  
Srtm Data

Recent production of a new radar-based global DEM by the TanDEM-X space mission has opened new options for geomorphometric analysis across multiple scales providing 0.4 arc second spatial resolution. However, the accuracy and suitability of this data has not been evaluated in such an extensive manner as for the widely exploited SRTM data. We present a validation of the vertical accuracy of TanDEM-X DEM product and evaluation of its suitability for landform classification in a forested karst area. The Geomorphons method was used for the automated landform classification focused on identification of dolines for which polygons of dolines mapped by expert-driven approach were used for validation. Airborne lidar data in the form of DSM and DTM were used as the reference dataset for validation of the DEM. The results show that the vertical RMSE of the TanDEM-X data is 3.42 m with respect to lidar DSM and 9.64 m with respect to lidar DTM. The identification of dolines by the geomorphon approach achieved 73 % with TanDEM-X, lower than for the lidar DTM (85 %).

scholarly journals Validation of TanDEM-X elevation data for a forested karst area in Slovakia (Central Europe)

2018 ◽  
Author(s):  
Peter Bandura ◽  
Michal Gallay
Keyword(s):  
Multiple Scales ◽  
Space Mission ◽  
Airborne Lidar ◽  
Karst Area ◽  
Lidar Data ◽  
Reference Dataset ◽  
Landform Classification ◽  
Elevation Data ◽  
Airborne Lidar Data ◽  
Srtm Data

Recent production of a new radar-based global DEM by the TanDEM-X space mission has opened new options for geomorphometric analysis across multiple scales providing 0.4 arc second spatial resolution. However, the accuracy and suitability of this data has not been evaluated in such an extensive manner as for the widely exploited SRTM data. We present a validation of the vertical accuracy of TanDEM-X DEM product and evaluation of its suitability for landform classification in a forested karst area. The Geomorphons method was used for the automated landform classification focused on identification of dolines for which polygons of dolines mapped by expert-driven approach were used for validation. Airborne lidar data in the form of DSM and DTM were used as the reference dataset for validation of the DEM. The results show that the vertical RMSE of the TanDEM-X data is 3.42 m with respect to lidar DSM and 9.64 m with respect to lidar DTM. The identification of dolines by the geomorphon approach achieved 73 % with TanDEM-X, lower than for the lidar DTM (85 %).

A gradient-constrained morphological filtering algorithm for airborne LiDAR

2013 ◽  
Vol 54 ◽  
pp. 288-296 ◽  
Author(s):  
Yong Li ◽  
Huayi Wu ◽  
Hanwei Xu ◽  
Ru An ◽  
Jia Xu ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Filtering Algorithm ◽  
Morphological Filtering

scholarly journals Canopy and Terrain Height Retrievals with ICESat-2: A First Look

2019 ◽  
Vol 11 (14) ◽  
pp. 1721 ◽  
Author(s):  
Amy L. Neuenschwander ◽  
Lori A. Magruder
Keyword(s):  
Sea Ice ◽  
Quantitative Assessment ◽  
Vantage Point ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Scientific Objective ◽  
Ice Cloud ◽  
Elevation Data ◽  
Airborne Lidar Data ◽  
Along Track

NASA’s Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) launched in fall 2018 and has since collected continuous elevation data over the Earth’s surface. The primary scientific objective is to measure the cryosphere for studies related to land ice and sea ice characteristics. The vantage point from space, however, provides the opportunity to measure global surfaces including oceans, land, and vegetation. The ICESat-2 mission has dedicated products to the represented surface types, including an along-track elevation profile of terrain and canopy heights (ATL08). This study presents the first look at the ATL08 product and the quantitative assessment of the canopy and terrain height retrievals as compared to airborne lidar data. The study also provides qualitative examples of ICESat-2 observations from selected ecosystems to highlight the broad capability of the satellite for vegetation applications. Analysis of the mission’s preliminary ATL08 data product accuracy using an ICESat-2 transect over a vegetated region of Finland indicates a 5 m offset in geolocation knowledge (horizontal accuracy) well within the 6.5 m mission requirement. The vertical RMSE for the terrain and canopy height retrievals for one transect are 0.85 m and 3.2 m respectively.

scholarly journals MERGING AIRBORNE LIDAR DATA AND SATELLITE SAR DATA FOR BUILDING CLASSIFICATION

2015 ◽  
Vol XL-4/W5 ◽  
pp. 227-232 ◽  
Author(s):  
T. Yamamoto ◽  
M. Nakagawa
Keyword(s):  
Data Acquisition ◽  
Urban Areas ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Attribute Data ◽  
Lidar Measurements ◽  
Map Generation ◽  
Geometrical Data ◽  
Sar Data ◽  
Airborne Lidar Data

A frequent map revision is required in GIS applications, such as disaster prevention and urban planning. In general, airborne photogrammetry and LIDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, attribute data acquisition and classification depend on manual editing works including ground surveys. In general, airborne photogrammetry and LiDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, these approaches classify geometrical attributes. Moreover, ground survey and manual editing works are finally required in attribute data classification. On the other hand, although geometrical data extraction is difficult, SAR data have a possibility to automate the attribute data acquisition and classification. The SAR data represent microwave reflections on various surfaces of ground and buildings. There are many researches related to monitoring activities of disaster, vegetation, and urban. Moreover, we have an opportunity to acquire higher resolution data in urban areas with new sensors, such as ALOS2 PALSAR2. Therefore, in this study, we focus on an integration of airborne LIDAR data and satellite SAR data for building extraction and classification.

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