scholarly journals Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)

2022 ◽  
Vol 270 ◽  
pp. 112857
Langning Huo ◽  
Eva Lindberg ◽  
Johan Holmgren
2016 ◽  
Vol 33 (4) ◽  
pp. 697-712 ◽  
R. Andrew Weekley ◽  
R. Kent Goodrich ◽  
Larry B. Cornman

AbstractAn image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.

R. A. Loberternos ◽  
W. P. Porpetcho ◽  
J. C. A. Graciosa ◽  
R. R. Violanda ◽  
A. G. Diola ◽  

Traditional remote sensing approach for mapping aquaculture ponds typically involves the use of aerial photography and high resolution images. The current study demonstrates the use of object-based image processing and analyses of LiDAR-data-generated derivative images with 1-meter resolution, namely: CHM (canopy height model) layer, DSM (digital surface model) layer, DTM (digital terrain model) layer, Hillshade layer, Intensity layer, NumRet (number of returns) layer, and Slope layer. A Canny edge detection algorithm was also performed on the Hillshade layer in order to create a new image (Canny layer) with more defined edges. These derivative images were then used as input layers to perform a multi-resolution segmentation algorithm best fit to delineate the aquaculture ponds. In order to extract the aquaculture pond feature, three major classes were identified for classification, including land, vegetation and water. Classification was first performed by using assign class algorithm to classify Flat Surfaces to segments with mean Slope values of 10 or lower. Out of these Flat Surfaces, assign class algorithm was then performed to determine Water feature by using a threshold value of 63.5. The segments identified as Water were then merged together to form larger bodies of water which comprises the aquaculture ponds. The present study shows that LiDAR data coupled with object-based classification can be an effective approach for mapping coastal aquaculture ponds. The workflow currently presented can be used as a model to map other areas in the Philippines where aquaculture ponds exist.

2015 ◽  
Vol 26 (08) ◽  
pp. 1550091 ◽  
Ju Li ◽  
Kai Yu ◽  
Ke Hu

Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weighting W and tightness T. These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection. To estimate the optimal stop time of iteration, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29623-29638 ◽  
Pengpeng Sun ◽  
Xiangmo Zhao ◽  
Zhigang Xu ◽  
Runmin Wang ◽  
Haigen Min

2019 ◽  
Vol 19 (1) ◽  
pp. 8-16
Zhitao Xiao ◽  
Lei Pei ◽  
Fang Zhang ◽  
Ying Sun ◽  
Lei Geng ◽  

Abstract In this paper, a new method based on phase congruency is proposed to measure pitch lengths and surface braiding angles of two-dimensional biaxial braided composite preforms. Lab space transform and BM3D (block-matching and 3D filter) are used first to preprocess the original acquired images. A corner detection algorithm based on phase congruency is then proposed to detect the corners of the preprocessed images. Pitch lengths and surface braiding angles are finally measured based on the detected corner maps. Experimental results show that our method achieves the automatic measurement of pitch lengths and the surface braiding angles of biaxial braided composite preforms with high accuracy.

2002 ◽  
Vol 32 (3) ◽  
pp. 509-518 ◽  
Jörgen Wallerman ◽  
Steve Joyce ◽  
Coomaren P Vencatasawmy ◽  
Håkan Olsson

The modern techniques of the global positioning system and geographic information system enable many new approaches to forestry planning problems. Using these it is possible to efficiently geoposition, store, and analyze each field measurement in a spatial context. This work is directed towards the application of a dynamic forestry planning system based on a forest map with very high spatial resolution created from geopositioned field plot data, instead of the traditional forest stand map. The new dynamic system is dependent on accurate methods to create a high-resolution map from a set of field measurements. This problem may be solved using the kriging spatial prediction (interpolation) method. The aim of this paper is to present and empirically evaluate a new kriging method side-by-side with global and stratified kriging. The new method uses the output from an edge-detection algorithm, here applied to Landsat TM image data, to increase the prediction accuracy. Prediction evaluation was made in terms of mean forest stem volume per hectare measured on circular field plots of 10 m radius. The new method showed a prediction root mean square error of 41% of the mean volume, compared with corresponding results of global, 58%, and stratified kriging, 45%.

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