scholarly journals TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 283-287
Author(s):  
N. Li ◽  
C. Liu ◽  
N. Pfeifer ◽  
J. F. Yin ◽  
Z.Y. Liao ◽  
...  
Keyword(s):  
Tensor Decomposition ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Observation Data ◽  
Multiple Features ◽  
Key Factor ◽  
Earth Observation Data ◽  
Airborne Lidar Data ◽  
Nearest Neighborhood

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.

scholarly journals TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 283-287
Author(s):  
N. Li ◽  
C. Liu ◽  
N. Pfeifer ◽  
J. F. Yin ◽  
Z.Y. Liao ◽  
...  
Keyword(s):  
Tensor Decomposition ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Observation Data ◽  
Multiple Features ◽  
Key Factor ◽  
Earth Observation Data ◽  
Airborne Lidar Data ◽  
Nearest Neighborhood

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.

scholarly journals A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 110
Author(s):  
Filippo Sarvia ◽  
Elena Xausa ◽  
Samuele De Petris ◽  
Gianluca Cantamessa ◽  
Enrico Borgogno-Mondino
Keyword(s):  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Observation Data ◽  
Key Factor ◽  
Ndvi Time Series ◽  
Earth Observation Data ◽  
Four Levels ◽  
Nw Italy ◽  
Sentinel 2

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.

scholarly journals Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

2017 ◽  
Vol 9 (8) ◽  
pp. 771 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Dunyong Zheng ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data

Classification of Airborne Lidar Data by Echo

2012 ◽  
Vol 500 ◽  
pp. 696-700 ◽  
Author(s):  
Sheng Yao Wang ◽  
Xi Min Cui ◽  
De Bao Yuan ◽  
Jing Jing Jin ◽  
Qiang Zhang
Keyword(s):  
Data Collection ◽  
Current Data ◽  
Airborne Lidar ◽  
Classification Method ◽  
Lidar Data ◽  
Collection System ◽  
Continuous Development ◽  
Data Collection System ◽  
Airborne Lidar Data

With the continuous development of Airborne Lidar hardware, the current data collection system will not only collect information on a single echo, multiple echo information also can be available. Through the analysis and discussion of echo principle, this paper compares and elaborates the characteristics of single-echo and multiple echo information, and introduces a filter classification method based on echo information, and illustrates that the method is simple and effective according to an example.

scholarly journals Automatic LOD0 classification of airborne LiDAR data in urban and non-urban areas

2018 ◽  
Vol 51 (1) ◽  
pp. 978-990
Author(s):  
J. Balado ◽  
L. Díaz-Vilariño ◽  
P. Arias ◽  
L. M. González-Desantos
Keyword(s):  
Urban Areas ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data
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