A progressive morphological filter for removing nonground measurements from airborne LIDAR data

By Keqi Zhang, Shu‐Ching Chen, D. Whitman, Mei-Ling Shyu, Jianhua Yan, Chengcui Zhang. IEEE Transactions on Geoscience and Remote Sensing, 2003

1,048 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/48735bb8dd02613f481e79ac7703d96c4831313f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Bruce Douglas Cook
2. Douglas C. Morton
3. Erle C. Ellis
4. Jonathan P. Dandois
5. Juha Hyyppä
6. Guang Zheng
7. Shu‐Ching Chen
8. Guangjian Yan

In Environmental Engineering

1. Erle C. Ellis
2. Jonathan P. Dandois
3. Juha Hyyppä
4. Xiaowei Yu
5. Markus Holopainen
6. Mikko Vastaranta
7. Bruce Douglas Cook
8. Douglas C. Morton

In Geology

1. Erle C. Ellis
2. Jonathan P. Dandois
3. Jan Hackenberg
4. Shu‐Ching Chen
5. Keqi Zhang
6. Jianhua Yan
7. Martin Mokros
8. Xiaoye Liu

Citations in the corpus

• NASA Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager (2013, background): “Classification of ground returns is performed with a progressive morphological filter [23].”
• High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision (2013, methodology): “different algorithm parameters, a form of optimization that is a common and recommended practice with other filtering algorithm packages (Evans & Hudak, 2007; Sithole & Vosselman, 2004; Tinkham et al., 2012; Zhang et al., 2003), and has previously been used across a range of different forest types, ”
• Automatic Construction of Building Footprints From Airborne LIDAR Data (2006, methodology): “are separated as a first step in the proposed framework using a progressive morphological filter [19].”
• A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments (2007, background): “The LISA statistic effectively identified potential commission errors and allowed the evaluation of the magnitude of each error.”
• International benchmarking of terrestrial laser scanning approaches for forest inventories (2018, methodology): “To remove false positives, a progressive morphological filter (Zhang et al., 2003) was applied which uses opening operation (erosion followed by dilation).”
• An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation (2016, background): “[10] developed a progressive morphological filter to remove non-ground measurements by comparing the elevation differences of original and morphologically opened surfaces with increasing window sizes.”
• Estimating Individual Tree Height and Diameter at Breast Height (DBH) from Terrestrial Laser Scanning (TLS) Data at Plot Level (2018, methodology): “A morphological filtering method [52] is used to separate ground points from non-ground points.”
• Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables (2016, methodology): “As with the DR data processing, this used the 326 progressive morphological filter, as outlined in Zhang et al. (2003), to identify ground returns.”