scholarly journals Study on Dune Morphology in Computer Mapping Based on 3S Technology

2021 ◽  
Author(s):  
Zhidan Ba
Keyword(s):  
Medial Axis ◽  
Vegetation Coverage ◽  
Differential Gps ◽  
Zonal Distribution ◽  
3S Technology ◽  
Long Term Effect ◽  
Object Based ◽  
Drift Potential ◽  
High Resolution Images ◽  
Windward Slope

Take the parabolic dune of Hobq desert in Inner Mongolia as research object. Based on the GIS platform by using differential GPS data and spatial interpolation to generate DEM, then using Multi-periods high resolution images to acquire the environmental background, at the same time combine with regional wind regime and vegetation condition to measure and analyze the morphology of the parabolic dune. The result shows that the parabolic dune showed U shape in plane, and dune arms point to the west which was also wind direction. The windward slope of longitudinal profile is gentler, while leeward slope is steeper. And cross section wasn’t symmetric. The dune’s average moving speed is 11.76 m/yr. Desert ridge line’s medial axis direction is WNW-ESE, in accord with the direction of prevailing wind and resultant drift potential. Artemisia Ordosicas mainly distribute on leeward slope, two arms, and the plane ground between them, and the annual average vegetation coverage decreased 0.95%. In the long-term effect of resultant wind, the dune keeps moving forward and Artemisia Ordosica between two arms show gradual natural stage recovery which presented zonal distribution. 3S technology has already become important research method in modern Aeolian sand morphology.

scholarly journals Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 320
Author(s):  
Emilio Guirado ◽  
Javier Blanco-Sacristán ◽  
Emilio Rodríguez-Caballero ◽  
Siham Tabik ◽  
Domingo Alcaraz-Segura ◽  
...  
Keyword(s):  
High Resolution ◽  
Optical Sensors ◽  
Biotic Interactions ◽  
Relevant Information ◽  
Vegetation Monitoring ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

scholarly journals SAR Image Segmentation Using Morphological Attribute Profiles

2014 ◽  
Vol XL-3 ◽  
pp. 39-44 ◽  
Author(s):  
M. Boldt ◽  
A. Thiele ◽  
K. Schulz ◽  
S. Hinz
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Region Growing ◽  
Object Based Image Analysis ◽  
Sar Image Segmentation ◽  
High Resolution Imagery ◽  
Object Based ◽  
High Resolution Images ◽  
Novel Method ◽  
Image Set

In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. <br><br> In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.

scholarly journals Object-Based Features for House Detection from RGB High-Resolution Images

2018 ◽  
Vol 10 (3) ◽  
pp. 451 ◽  
Author(s):  
Renxi Chen ◽  
Xinhui Li ◽  
Jonathan Li
Keyword(s):  
High Resolution ◽  
Object Based ◽  
High Resolution Images

Monitoring of vegetation coverage based on high-resolution images

2007 ◽  
Vol 9 (4) ◽  
pp. 256-261 ◽  
Author(s):  
Li Zhang ◽  
Li-juan Li ◽  
Li-qiao Liang ◽  
Jiu-yi Li
Keyword(s):  
High Resolution ◽  
Vegetation Coverage ◽  
High Resolution Images

scholarly journals Assessing the Crown Closure of Nypa on UAV Images using Mean-Shift Segmentation Algorithm

2018 ◽  
Vol 9 (3) ◽  
pp. 722
Author(s):  
Robert Parulian Silalahi ◽  
I Nengah Surati Jaya ◽  
Tatang Tiryana ◽  
Fairus Mulia
Keyword(s):  
Accuracy Assessment ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Visual Interpretation ◽  
Detection And Identification ◽  
Object Based ◽  
High Resolution Images ◽  
Uav Images ◽  
Crown Closure ◽  
Parameter Values

<p>Utilization of very high-resolution images becomes a new trend in forest management, particularly in the detection and identification of forest stand variables. This paper describes the use of mean-shift segmentation algorithm on unmanned aerial vehicles (UAV) images to measure crown closure of nypa (Nypa fructicans) and gap. The 27 combinations of the parameter values such as spatial radius (hs), range radius (hr), and minimum region size (M). Gap detection and nypa crown closure measurements were performed using a hybrid between pixel-based (maximum likelihood classifier) and object-based approaches (segmentation).  For evaluation of the approach performance, the accuracy assessment was done by comparing object-based classification results (segmentation) and visual interpretation (ground check). The study found that the best combination of segmentation parameter was the combination of hs 10, hr 10 and M 50, with the overall accuracy of 76,6% and kappa accuracy of 55.7%.</p>

scholarly journals HYBRID OPTIMIZATION OF OBJECT-BASED CLASSIFICATION IN HIGH-RESOLUTION IMAGES USING CONTINOUS ANT COLONY ALGORITHM WITH EMPHASIS ON BUILDING DETECTION

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 271-279
Author(s):  
E. Tamimi ◽  
H. Ebadi ◽  
A. Kiani
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Computational Time ◽  
Support Vector ◽  
Building Detection ◽  
Svm Classification ◽  
Spectral Bands ◽  
Object Based ◽  
High Resolution Images ◽  
Independent Features

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

scholarly journals The second Chinese glacier inventory: data, methods and results

2015 ◽  
Vol 61 (226) ◽  
pp. 357-372 ◽  
Author(s):  
Wanqin Guo ◽  
Shiyin Liu ◽  
Junli Xu ◽  
Lizong Wu ◽  
Donghui Shangguan ◽  
...  
Keyword(s):  
Shuttle Radar Topography Mission ◽  
Differential Gps ◽  
Band Ratio ◽  
Glacier Inventory ◽  
Positioning Errors ◽  
Covered Area ◽  
Digital Elevation ◽  
Acceptable Quality ◽  
High Resolution Images ◽  
Elevation Model

AbstractThe second Chinese glacier inventory was compiled based on 218 Landsat TM/ETM+ scenes acquired mainly during 2006–10. The widely used band ratio segmentation method was applied as the first step in delineating glacier outlines, and then intensive manual improvements were performed. The Shuttle Radar Topography Mission digital elevation model was used to derive altitudinal attributes of glaciers. The boundaries of some glaciers measured by real-time kinematic differential GPS or digitized from high-resolution images were used as references to validate the accuracy of the methods used to delineate glaciers, which resulted in positioning errors of ±10 m for manually improved clean-ice outlines and ±30 m for manually digitized outlines of debris-covered parts. The glacier area error of the compiled inventory, evaluated using these two positioning accuracies, was ±3.2%. The compiled parts of the new inventory have a total area of 43 087 km2, in which 1723 glaciers were covered by debris, with a total debris-covered area of 1494 km2. The area of uncompiled glaciers from the digitized first Chinese glacier inventory is ∼8753 km2, mainly distributed in the southeastern Tibetan Plateau, where no images of acceptable quality for glacier outline delineation can be found during 2006–10.

Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images

2020 ◽  
Vol 163 ◽  
pp. 171-186 ◽  
Author(s):  
Zhen Guan ◽  
Amr Abd-Elrahman ◽  
Zhen Fan ◽  
Vance M. Whitaker ◽  
Benjamin Wilkinson
Keyword(s):  
High Resolution ◽  
Leaf Area ◽  
Object Based ◽  
High Resolution Images
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