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9th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Huang, Xincai Bai, Huang Li, Sheng Zhejiang Univ Technol Coll Informat Engn Zhejiang Prov Key Lab Signal Proc Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9781479943159

Automatic segmentation of aerial images has been a challenging area of research in recent years. Among numerous image segmentation methods, the level set method has received a great deal of attention which could represent contours or surfaces with complex topology and change their topology in a natural way. The solution of classic level set model, however, can be easily trapped into a local minimum. To overcome this problem, a novel modified dual Chan-Vese model is proposed in this paper. This proposed model is composed of two contours, which evolve towards the edges of objects from inside of the objects and outside of the objects. By reducing the differences between the interior contour and the external contour, the proposed model can partly prevent the solution of the level set method from a local minimum. Experiments show that the proposed model can obtain exact aerial image segmentation.

关键词： aerial image segmentation Level Set Model Chan-Vese Model Dual Chan-Vese Model

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Semantic segmentation based mapping systems for the safe and precise landing of flying vehicles 22

Semantic segmentation based mapping systems for the safe and...

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22nd International-Federation-of-Automatic-Control (IFAC) Symposium on Automatic Control in Aerospace (ACA)

作者： Dhami, Harsimret Singh Ignatyev, Dmitry Tsourdos, Antonios Cranfield Univ Bedford MK43 0AL England Cranfield Univ Sch Aerosp Transport & Mfg Autonomous Syst & Control Bedford MK43 0AL England Cranfield Univ Sch Aerosp Transport & Mfg Autonomous & Cyber Phys Syst Ctr Bedford MK43 0AL England

ISBN: (纸本)9781713867890

Unmanned aerial Systems (UAS) are a promising technology for many areas, including transportation, agriculture, inspection, and rescue missions. However, to enable a high level of autonomy,including Beyond Visual Line of Sight (BVLOS) flight, the drones should be able to perform safe landings in unknown areas without an operator. Hence there is a need for development of safe landing methods for autonomous *** autonomous UAVs can often be operated more economically than the conventional manned aircraft. As technology advances, autonomous UAVs are expected to play an increasingly important role in a variety of industries and *** this paper we have explored a semantic segmentation-based approach for the problem of autonomous landing.

关键词： Semantic segmentation Computer vision aerial image segmentation Unmanned aerial Vehicles (UAVs) Autonomous Landing

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Comparative Study Between Real-Time and Non-Real-Time segmentation Models on Flooding Events

Comparative Study Between Real-Time and Non-Real-Time Segmen...

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： Safavi, Farshad Chowdhury, Tashnim Rahnemoonfar, Maryam Univ Maryland Baltimore Cty Comp Vis & Remote Sensing Lab Baltimore MD 21228 USA

ISBN: (纸本)9781665439022

Scene understanding of aerial imagery is essential for proper emergency response during catastrophic events such as hurricanes, earthquakes, and floods. Unmanned aerial Vehicles (UAVs) capture aerial images and analyze the context by passing images into a semantic segmentation model for monitoring damaged areas. However, the state-of-the-art semantic segmentation models are mainly trained and evaluated on ground-based datasets such as Cityscapes, MS-COCO, and CamVid, unsuitable for aerial image segmentations. For example, extracted features from objects in aerial perspective are distinct from objects on the ground view. Hence, neural networks cannot properly segment an aerial scene, especially on deformed or damaged objects during disasters. This research analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under adversarial settings. Furthermore, we train several models on the FloodNet dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded-buildings vs. non-flooded buildings or flooded-roads vs. non-flooded roads. In this research, real-time UNet-MobileNetV3 yields 59.3% test mIoU while non-real-time PSPNet [1] attains 79.7% test mIoU on the FloodNet, demonstrating the trade-off between accuracy and efficiency in the segmentation models.

关键词： Computer Vision Deep Learning Convolutional Neural Network(CNN) aerial image segmentation Real-Time Semantic segmentation

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Semantic segmentation based mapping systems for the safe and precise landing of flying vehicles

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IFAC-PapersOnLine 2022年第22期55卷 310-315页

作者： Harsimret Singh Dhami Dmitry Ignatyev Antonios Tsourdos Research Student Cranfield University Bedford MK430AL UK Senior Research Fellow in Autonomous Systems and Control School of Aerospace Transport and Manufacturing Cranfield University Bedford MK430AL UK Professor Head of the Autonomous and Cyber-Physical Systems Centre School of Aerospace Transport and Manufacturing Cranfield University Bedford MK430AL UK

Unmanned aerial Systems (UAS) are a promising technology for many areas, including transportation, agriculture, inspection, and rescue missions. However, to enable a high level of autonomy, including Beyond Visual Line of Sight (BVLOS) filght, the drones should be able to perform safe landings in unknown areas without an operator. Hence there is a need for development of safe landing methods for autonomous drones. The autonomous UAVs can often be operated more economically than the conventional manned aircraft. As technology advances, autonomous UAVs are expected to play an increasingly important role in a variety of industries and applications. In this paper we have explored a semantic segmentation-based approach for the problem of autonomous landing.

关键词： Semantic segmentation Computer vision aerial image segmentation Unmanned aerial Vehicles (UAVs) Autonomous Landing

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Automatic aerial image segmentation based on a Modified Chan-Vese Algorithm

Automatic Aerial Image Segmentation based on a Modified Chan...

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5th International Congress on image and Signal Processing (CISP)

作者： Ahmadi, Parvin Sadri, Saeed Amirfattahi, Rassoul Gheissari, Niloofar Isfahan Univ Technol Digital Signal Proc Res Lab Dept Elect & Comp Engn Esfahan *** Iran

ISBN: (纸本)9781467309646

Automatic segmentation of aerial images has been a challenging task in recent years. Region-based active contour of Chan-Vese has been proposed to detect objects in a given image. This algorithm is more powerful than classical edge-based active contour algorithms. In this paper, aerial images are automatically segmented into a number of homogeneous areas using Chan-Vese model implemented by Narrow Band Level Set method with re-initialization together with extracting color and texture features. For this purpose, a variety of different color and texture features have been tested. The results show that incorporation of Gabor filters in HSV color space leads the most accurate results.

关键词： aerial image segmentation color and texture features Chan-Vese model

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Application of Satellite image segmentation for Urban Planning Optimization

Application of Satellite Image Segmentation for Urban Planni...

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作者： Vladimir Khryashchev Leonid Ivanovsky Anna Ostrovskaya Alexander Semenov People’s Friendship University of Russia (RUDN University) P.G.Demidov Yaroslavl State University

This article presents research results of a convolution neural network for building detection on high-resolution aerial images of Planet *** index was used for analysis of the quality of machine learning *** index of similarity compares results of algorithms with real *** masks were sliced on smaller parts together with images before training of developed *** convolution neural network was launched on NVIDIA DGX-1 supercomputer,which was provided by AIcenter of P.G Demidov Yaroslavl State *** problem of building detection on satellite images can be put into practice for urban planning,building control,search of the best locations for outlets etc.

关键词： machine learning aerial image segmentation building detection

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Automatic aerial image segmentation based on a Modified Chan-Vese Algorithm

Automatic Aerial Image Segmentation based on a Modified Chan...

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International Congress on image and Signal Processing

作者： Parvin Ahmadi Saeed Sadri Rassoul Amirfattahi Niloofar Gheissari Department of Electrical and Computer Engineering Isfahan University of Technology

ISBN: (纸本)9781467309653

关键词： aerial image segmentation Color and texture features Chan-Vese model

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segmentation and Classification of Geoenvironmental Zones of Interest in aerial images Using the Bounded Irregular Pyramid 6th

Segmentation and Classification of Geoenvironmental Zones of...

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6th International Workshop on Computational Topology in image Context (CTIC)

作者： Calderon, Mariletty Marfil, Rebeca Bandera, Antonio Univ Malaga Dept Tecnol Elect Campus Univ Teatinos E-29071 Malaga Spain

ISBN: (纸本)9783319394404;9783319394411

The goal of this work is to automatically detect and classify a set of geoenvironmental zones of interest in panchromatic aerial images. Focused on a specific area, the zones to be detected are vegetation/mangrove, degradation/desertification, interface water-sediment and plain. These zones are very interesting from a geological point of view due to their spatial distribution and interrelation, which contribute to evaluate the natural anthropic impact level. The approach to unsupervisedly extract these zones from an input image has two steps. Firstly, the image is automatically segmented in homogeneous colored regions using the Bounded Irregular Pyramid (BIP). The BIP is a hierarchy of successively reduced graphs which produces accurate segmentation results with a low computational cost. Secondly, each obtained region is classified using texture features to determine if it belongs to one of the geoenvironmental zones of interest. As texture features, we have evaluated two variations of the Local Binary Pattern (LBP) descriptor: the Extended-LBP (ELBP) and the LBP variance (LBPV). Both methods include a local contrast measure. For classifying the obtained features, the Support Vector Machine (SVM) has been employed. At this stage, we have evaluated the use of linear and radial basis function (RBF) kernels. The whole framework was tested using images obtained from our specific area of interest: the location of Carenero, Miranda state (Venezuela), in years 1936 and 1992. They allow to study the variation of the geoenvironmental zones of interest of this location in this period of time. These images are low quality images and present significant variations in illumination. This makes difficult the texture classification of their zones. However, the obtained results show that the proposed approach provides good results in terms of identification of zones of geoenviromental interest in these images.

关键词： aerial image segmentation Irregular pyramid Texture classification

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A modified multiphase level set evolution scheme for aerial image segmentation

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2007年第7期21卷 1195-1212页

作者： Wei, Wang Xin, Yang Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China

This paper describes an innovative aerial images segmentation algorithm. The algorithm is based upon the knowledge of image multiscale geometric analysis using contourlet transform, which can extract the image's intrinsic geometrical structure efficiently. The contourlet transform is introduced to represent the most distinguished and the rotation invariant features of the image. A modified Mumford-Shah model is applied to segment the aerial image by a multifeature level set evolution. To avoid possible local minima in the level set evolution, we adjust the weighting coefficients of the multiscale features in different evolution periods, i.e. the global features have bigger weighting coefficients at the beginning stages which roughly de. ne the shape of the contour, then bigger weighting coefficients are assigned to the detailed features for segmenting the precise shape. When the algorithm is applied to segment the aerial images with several classes, satisfied experimental results are achieved by the proposed method.

关键词： aerial image segmentation multiscale geometric analysis the contourlet transform Mumford-Shah model

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