scholarly journals Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification

2020 ◽  
Vol 12 (7) ◽  
pp. 1081 ◽  
Author(s):  
Mohamed Barakat A. Gibril ◽  
Bahareh Kalantar ◽  
Rami Al-Ruzouq ◽  
Naonori Ueda ◽  
Vahideh Saeidi ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Unmanned Aerial Vehicle ◽  
Urban Areas ◽  
Integrated Approach ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Surface Model ◽  
Urban Landscapes ◽  
Digital Surface Model ◽  
Aerial Vehicle

Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In this study, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale (MS) supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR orthomosaic and digital surface model (DSM). The integrated approach commenced through a preliminary MS image segmentation parameter selection, followed by the application of three supervised ML models, namely, random forest (RF), support vector machine (SVM), and decision tree (DT). These models were implemented at the optimal MS levels to identify preliminary information, such as the optimal segmentation level(s) and relevant features, for extracting 12 land use/land cover (LULC) urban classes from the fused datasets. Using the information obtained from the first phase of the analysis, detailed MS classification was iteratively conducted to improve the classification accuracy and derive the final urban LULC maps. Two UAV-based datasets were used to develop and assess the effectiveness of the proposed framework. The hierarchical classification of the pilot study area showed that the RF was superior with an overall accuracy (OA) of 94.40% and a kappa coefficient (K) of 0.938, followed by SVM (OA = 92.50% and K = 0.917) and DT (OA = 91.60% and K = 0.908). The classification results of the second dataset revealed that SVM was superior with an OA of 94.45% and K of 0.938, followed by RF (OA = 92.46% and K = 0.916) and DT (OA = 90.46% and K = 0.893). The proposed framework exhibited an excellent potential for the detailed mapping of heterogeneous urban landscapes from the fusion of UHSR orthophoto and DSM images using various ML models.

Evaluation of various setups of hydraulic structures in wild creek by River Flow2D

2020 ◽  
Author(s):  
Chin-Hsiang Tu ◽  
Hung-Pin Huang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
River Flow ◽  
Rainfall Data ◽  
Hydraulic Structures ◽  
Surface Model ◽  
Digital Surface Model ◽  
Check Dam ◽  
Aerial Photos ◽  
Aerial Vehicle ◽  
Movable Bed

<p>In Taiwan, the hydraulic structures of groundsill, check dam and embankment are frequently used in wild creek in order to prevent longitudinal and lateral scour. The benefit of these structures could not be numerically evaluated before construction without movable bed computational software. In recent years, the downstream scour-and-fill of hydraulic structures in wild creek could be carried out by software of River Flow 2D. This study used this software to evaluate the various setups of hydraulic structures in Jianshi, Hsinchu. Before carrying out software, the unmanned aerial vehicle (UAV) was operated to capture aerial photos of watershed. Then, the digital surface model (DSM) and orthomosaic photos were produced by Pix4Dmapper. Because most of wild creeks have no vegetation on their own creek bed, the DTM could be replaced by DSM. Associated with the various setups of hydraulic structures, Global mapper, QGIS and designed rainfall data, the software of River Flow 2D could give the downstream scour-and-fill of various setups of hydraulic structures. And, the convenient setup could be selected after evaluating the various setups of hydraulic structures.</p>

scholarly journals Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model

2021 ◽  
Vol 11 (13) ◽  
pp. 6072
Author(s):  
Nicla Maria Notarangelo ◽  
Arianna Mazzariello ◽  
Raffaele Albano ◽  
Aurelia Sole
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Urban Morphology ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Surface Model ◽  
Feature Descriptor ◽  
Digital Surface Model ◽  
Building Extraction ◽  
Area Of Interest

Automatic building extraction from high-resolution remotely sensed data is a major area of interest for an extensive range of fields (e.g., urban planning, environmental risk management) but challenging due to urban morphology complexity. Among the different methods proposed, the approaches based on supervised machine learning (ML) achieve the best results. This paper aims to investigate building footprint extraction using only high-resolution raster digital surface model (DSM) data by comparing the performance of three different popular supervised ML models on a benchmark dataset. The first two methods rely on a histogram of oriented gradients (HOG) feature descriptor and a classical ML (support vector machine (SVM)) or a shallow neural network (extreme learning machine (ELM)) classifier, and the third model is a fully convolutional network (FCN) based on deep learning with transfer learning. Used data were obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and cover the urban areas of Vaihingen an der Enz, Potsdam, and Toronto. The results indicated that performances of models based on shallow ML (feature extraction and classifier training) are affected by the urban context investigated (F1 scores from 0.49 to 0.81), whereas the FCN-based model proved to be the most robust and best-performing method for building extraction from a high-resolution raster DSM (F1 scores from 0.80 to 0.86).

scholarly journals Monitoring the Damage of Armyworm in Summer Corn by Unmanned Aerial Vehicle Imaging

2020 ◽  
Author(s):  
mingzheng zhang ◽  
Xinsheng Wang ◽  
Jinghao Xue ◽  
Wei Su ◽  
Dehai Zhu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Regional Scale ◽  
Support Vector ◽  
Surface Model ◽  
Automated Identification ◽  
Aerial Vehicle ◽  
Corn Plants ◽  
Uav Images ◽  
Uav Image ◽  
Sentinel 2

Abstract Background: Monitoring armyworm (Mythimna separata Walker) damage in crops requires timely, rapid and accurate observations to avoid severe yield losses. Results: The Random Forest (RF) classifier was more effective at automatically and accurately monitoring armyworm damage compared with Support Vector Machine (SVM), Multilayer Perceptron Classifier (MLPC) and Naive Bayes Classifier (NB) classifiers. Furthermore, the incorporation of an Unmanned Aerial Vehicle (UAV) image-generated digital surface model improved the performance of the RF classifier, increasing the F-score from 0.985 and 0.970 to 0.997 and 0.994, and increasing the Kappa coefficient from 0.955 to 0.990. In addition, we found that Band 3 (735 nm) of the UAV image and Band 6 (740 nm) of a coincident Sentinel-2 image were not sensitive to an armyworm infestation in this study. Conclusions: We developed an accurate algorithm for the automated identification of armyworm-damaged corn plants using UAV images at the field scale. The study also indicated the feasibility of the developed method for monitoring corn armyworm damage at regional scale when combined with Sentinel-2 images.

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

scholarly journals Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

2021 ◽  
Vol 13 (10) ◽  
pp. 1997
Author(s):  
Joan Grau ◽  
Kang Liang ◽  
Jae Ogilvie ◽  
Paul Arp ◽  
Sheng Li ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Riparian Zone ◽  
Agricultural Landscapes ◽  
Agricultural Watershed ◽  
Riparian Zones ◽  
Surface Model ◽  
Low Resolution ◽  
Channel Network ◽  
Aerial Vehicle

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.

Road Image Segmentation using Unmanned Aerial Vehicle Images and DeepLab V3+ Semantic Segmentation Model

2021 ◽  
Author(s):  
Mat Nizam Mahmud ◽  
Muhammad Khusairi Osman ◽  
Ahmad Puad Ismail ◽  
Fadzil Ahmad ◽  
Khairul Azman Ahmad ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Aerial Vehicle

scholarly journals SAR AND OBLIQUE AERIAL OPTICAL IMAGE FUSION FOR URBAN AREA IMAGE SEGMENTATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 639-642 ◽  
Author(s):  
J. Fagir ◽  
A. Schubert ◽  
M. Frioud ◽  
D. Henke
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Single Channel ◽  
Research Area ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Surface Model ◽  
Digital Surface Model ◽  
Oblique View ◽  
Optical Images

The fusion of synthetic aperture radar (SAR) and optical data is a dynamic research area, but image segmentation is rarely treated. While a few studies use low-resolution nadir-view optical images, we approached the segmentation of SAR and optical images acquired from the same airborne platform – leading to an oblique view with high resolution and thus increased complexity. To overcome the geometric differences, we generated a digital surface model (DSM) from adjacent optical images and used it to project both the DSM and SAR data into the optical camera frame, followed by segmentation with each channel. The fused segmentation algorithm was found to out-perform the single-channel version.

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