Road Segmentation in Aerial Images by Exploiting Road Vector Data

2013 ◽  
Author(s):  
Jiangye Yuan ◽  
Anil M. Cheriyadat
Keyword(s):  
Aerial Images ◽  
Vector Data ◽  
Road Segmentation

scholarly journals Review of Road Detection and Segmentation Technique Using CNN

2020 ◽  
pp. 161-165
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Aerial Images ◽  
Road Detection ◽  
Insurance Plan ◽  
Toll Road ◽  
White Pixel ◽  
Road Segmentation ◽  
Matlab Programming ◽  
The Relationship

This research proposes form shape mounted on “the deep convolutional neural network (CNN) for the detection of roads and the segmentation of aerial pix. Those images are received by using a UAV. The photograph segmentation set of rules has two levels: the studying segment and the working phase. The aerial images of the data deteriorated into their coloration additives, had been pre-processed in matlab on hue, after which divided into small 33 × 33 pixel packing containers the usage of a sliding container set of rules. CNN was once designed with matconvnet and had the accompanying structure: 4 convolutional levels, 4 grouping stages, a relu layer, a totally linked layer, and a softmax layer. The entire community has been organized for the use of 2,000 boxes. CNN was implemented the use of matlab programming on the gpu and the outcomes are promising. The CNN output offers pixel-by means of-pixel records, which class it has a location with (road / non-road). White pixel and choppy terrain are known as "0" (dark). Monitoring roads is a troublesome venture in aerial picture segmentation due to quite more than a few sizes and surfaces. One of the vastest steps in CNN training is the pre-processing phase. Due to toll road segmentation, dismissal structures and complexity enhancement have been applied.” this is an audited article on the relationship between representative upkeep techniques with work pleasure and responsibility in insurance plan businesses.

scholarly journals VARIABLE SELECTION FOR ROAD SEGMENTATION IN AERIAL IMAGES

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 297-304 ◽  
Author(s):  
S. Warnke ◽  
D. Bulatov
Keyword(s):  
Variable Selection ◽  
Minimum Cost ◽  
Optimization Methods ◽  
Training Data ◽  
Aerial Images ◽  
Misclassification Error ◽  
3D Data ◽  
Elevation Data ◽  
Cost Paths ◽  
Road Segmentation

For extraction of road pixels from combined image and elevation data, Wegner et al. (2015) proposed classification of superpixels into road and non-road, after which a refinement of the classification results using minimum cost paths and non-local optimization methods took place. We believed that the variable set used for classification was to a certain extent suboptimal, because many variables were redundant while several features known as useful in Photogrammetry and Remote Sensing are missed. This motivated us to implement a variable selection approach which builds a model for classification using portions of training data and subsets of features, evaluates this model, updates the feature set, and terminates when a stopping criterion is satisfied. The choice of classifier is flexible; however, we tested the approach with Logistic Regression and Random Forests, and taylored the evaluation module to the chosen classifier. To guarantee a fair comparison, we kept the segment-based approach and most of the variables from the related work, but we extended them by additional, mostly higher-level features. Applying these superior features, removing the redundant ones, as well as using more accurately acquired 3D data allowed to keep stable or even to reduce the misclassification error in a challenging dataset.

scholarly journals Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network

2019 ◽  
Vol 9 (22) ◽  
pp. 4825 ◽  
Author(s):  
Tamara Alshaikhli ◽  
Wen Liu ◽  
Yoshihisa Maruyama
Keyword(s):  
Traffic Management ◽  
City Planning ◽  
Road Networks ◽  
Good Alternative ◽  
Aerial Images ◽  
Training Dataset ◽  
Automated Method ◽  
Learning Techniques ◽  
Manual Methods ◽  
Road Segmentation

Updating road networks using remote sensing imagery is among the most important topics in city planning, traffic management and disaster management. As a good alternative to manual methods, which are considered to be expensive and time consuming, deep learning techniques provide great improvements in these regards. One of these techniques is the use of deep convolution neural networks (DCNNs). This study presents a road segmentation model consisting of a skip connection of U-net and residual blocks (ResBlocks) in the encoding part and convolution layers (Conv. layer) in the decoding part. Although the model uses fewer residual blocks in the encoding part and fewer convolution layers in the decoding part, it produces better image predictions in comparison with other state-of-the-art models. This model automatically and efficiently extracts road networks from high-resolution aerial imagery in an unexpansive manner using a small training dataset.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

دراسة الخصائص الجيومورفولوجية لطيات عين زالة ورافان وبطمة باستخدام معطيات التحسس النائي

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
ريان غازي ذنون ◽  
صهيب حميد الخفاجي
Keyword(s):  
Vector Data

استخدمت معطيات التحسس النائي في دراسة جيومورفولوجية لطيات عين زالة ورافان وبطمة شمالي العراق. بغية تحديد الاشكال الارضية وتمثيلها بهيأة خارطة جيومورفولوجية ، تم اعتماد المنهج الاستقرائي الذي يتضمن عمليتين مترابطتين هما: الملاحظة الجيولوجية وعلاقتها بالأشكال الظاهرة وذلك لتحديد السمات الجيومورفولوجية المميزة لمنطقة الدراسة، وقد استندت عملية الاستقراء هذه على نتائح التفسير البصري  للمرئية الفضائية المستخدمة في الدراسة الحالية. تم تحليل وتصنيف الاشكال الارضية في منطقة الدراسة حسب منشأها التكويني باستخدام مرئية فضائية للقمر الاصطناعي (Sentitial-2) والتي تتصف بقدرة تمييز مكانية قدرها (10) امتار. اذ تم ادخال المرئية الملونة في برنامج (ArcGis 10.3) لكي يتم تحديد الوحدات الجيومورفولوجية ورسمها من خلال اسلوب التمثيل الاتجاهي للبيانات (Vector Data). اسفرت النتائج عن تحديد عدد من الوحدات الجيومورفولوجية مثلت على خارطة معدة لهذا الغرض، وقد تم توثيق هذه الوحدات حقليا. لقد شهدت منطقة الدراسة تغيرات جوهرية في أنماط الغطاء الأرضي وتحويرا لبعض من الاشكال الجيومورفولوجية الظاهرة في المنطقة خلال الفترات الماضية الممتدة منذ الثمانينات من القرن الماضي وحتى يومنا هذ نتيجة بناء سد الموصل لذا فقد تم مراقبة التغييرات السابقة بالاستعانة بالمرئيات الفضائية المتعاقبة زمنيا والمعالجة رقميا بطريقة التصنيف الموجه وقد اسفرت النتائج عن تحديد الوضع الجيومورفولوجي للغطاء الارضي الحالي والتغيرات الحاصلة فيه بعد انشاء السد

Application of remote sensing methods for assessment of anthropogenic transformation of rangelands

2000 ◽  
pp. 16-25
Author(s):  
E. I. Rachkovskaya ◽  
S. S. Temirbekov ◽  
R. E. Sadvokasov
Keyword(s):  
Remote Sensing ◽  
New Technology ◽  
Field Studies ◽  
Digital Data ◽  
Aerial Images ◽  
Expert Assessment ◽  
Automated Classification ◽  
Anthropogenic Transformation ◽  
Initial Processing ◽  
Remote Sensing Methods

Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.

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