Desertification Detection in Makkah Region based on Aerial Images Classification

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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Classification Techniques Of High Resolution Aerial Images: A Survey Of Commonly A.I Techniques

10.36458/1253-000-032-014 ◽

2018 ◽

pp. 304

Author(s):

نداء فليح حسن ◽

علي صبار محسن

Keyword(s):

High Resolution ◽

Aerial Images ◽

Classification Techniques

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RESEARCH ON AUTOMATIC EXTRACTION OF DEFOCUS IMAGES IN UAV AERIAL IMAGES

Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) ◽

10.2208/jscejcei.75.2_i_106 ◽

2019 ◽

Vol 75 (2) ◽

pp. I_106-I_113

Author(s):

Etsuji KITAGAWA ◽

Ryo KATO ◽

Satoshi ABIKO ◽

Takumi TSUMURA ◽

Yusuke NAKATANI

Keyword(s):

Aerial Images ◽

Automatic Extraction

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Mapping of landscape-morphological structure of the Northern-Western part of Anapa bay bar by digital high resolution aerial images

Geodesy and Cartography ◽

10.22389/0016-7126-2017-927-9-22-29 ◽

2017 ◽

Vol 927 (9) ◽

pp. 22-29

Author(s):

V.I. Kravtsovа ◽

E.R. Chalova

Keyword(s):

High Resolution ◽

Morphological Structure ◽

Middle Part ◽

Aerial Images ◽

Medical Resource ◽

Sea Waves ◽

Coast Line ◽

Waves Interaction ◽

Account Due ◽

Digital Landscape

Anapa bay bar is a valuable recreational-medical resource. Digital landscape-morphological mapping of its the Northern-Western part was created by digital aero survey materials for monitoring of its statement. Compiled maps show that in the Western part of region dune belt is degradated, front dune hills destroyed due to spreading of settlement Veselovka buildings to beach, and due to mass enactments carrying out at bay bar of lake Solenoe. Here it is necessary to decide the problem of defense from waves flooding by construction of artificial hills. The middle part of region, around Bugaz lagoon, is using for unregulated recreation of extreme sportsmen – windsurfing and kiting – with seasonal recreation in camping from tent-city and campers. Many short roads to sea beach, orthogonal to coast line, have been transformed to corridors of blowing and sea waves interaction to lagoon lowland with dune belt destroying. In the Eastern part of region, at Bugaz bay bar, dune belt is conserve, it changes under natural sea and wind processes action. At some places sea waves are erode windward front dune slope. Just everywhere sand accumulative trains are forming at leeward slope of front dune. Showed peculiarities of landscape morphological structure mast be taken in account due treatment of measures for bay bar defense and keeping.

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Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

Remote Sensing ◽

10.3390/rs11101157 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1157 ◽

Cited By ~ 8

Author(s):

Jorge Fuentes-Pacheco ◽

Juan Torres-Olivares ◽

Edgar Roman-Rangel ◽

Salvador Cervantes ◽

Porfirio Juarez-Lopez ◽

...

Keyword(s):

Precision Agriculture ◽

Image Data ◽

Ground Truth ◽

Aerial Images ◽

Aerial Image ◽

Data Set ◽

Visual Appearance ◽

Aerial Robots ◽

Lighting Conditions ◽

Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

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Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

Applied Sciences ◽

10.3390/app9061128 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1128 ◽

Cited By ~ 12

Author(s):

Yundong Li ◽

Wei Hu ◽

Han Dong ◽

Xueyan Zhang

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Hurricane Sandy ◽

Training Data ◽

Aerial Images ◽

Detection Methods ◽

Single Shot ◽

Data Set ◽

Augmentation Strategies ◽

Post Disaster

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

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Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) ◽

10.1109/wacv48630.2021.00220 ◽

2021 ◽

Author(s):

Jingru Yi ◽

Pengxiang Wu ◽

Bo Liu ◽

Qiaoying Huang ◽

Hui Qu ◽

...

Keyword(s):

Object Detection ◽

Aerial Images ◽

Oriented Object

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InSAR Monitoring of Landslide Activity in Dominica

Remote Sensing ◽

10.3390/rs13040815 ◽

2021 ◽

Vol 13 (4) ◽

pp. 815

Author(s):

Mary-Anne Fobert ◽

Vern Singhroy ◽

John G. Spray

Keyword(s):

Risk Mitigation ◽

Disaster Mitigation ◽

Aerial Images ◽

Landslide Inventory ◽

Landslide Risk ◽

Susceptibility Map ◽

Interferometric Synthetic Aperture Radar ◽

Rainfall Events ◽

Susceptibility Maps ◽

Digital Elevation

Dominica is a geologically young, volcanic island in the eastern Caribbean. Due to its rugged terrain, substantial rainfall, and distinct soil characteristics, it is highly vulnerable to landslides. The dominant triggers of these landslides are hurricanes, tropical storms, and heavy prolonged rainfall events. These events frequently lead to loss of life and the need for a growing portion of the island’s annual budget to cover the considerable cost of reconstruction and recovery. For disaster risk mitigation and landslide risk assessment, landslide inventory and susceptibility maps are essential. Landslide inventory maps record existing landslides and include details on their type, location, spatial extent, and time of occurrence. These data are integrated (when possible) with the landslide trigger and pre-failure slope conditions to generate or validate a susceptibility map. The susceptibility map is used to identify the level of potential landslide risk (low, moderate, or high). In Dominica, these maps are produced using optical satellite and aerial images, digital elevation models, and historic landslide inventory data. This study illustrates the benefits of using satellite Interferometric Synthetic Aperture Radar (InSAR) to refine these maps. Our study shows that when using continuous high-resolution InSAR data, active slopes can be identified and monitored. This information can be used to highlight areas most at risk (for use in validating and updating the susceptibility map), and can constrain the time of occurrence of when the landslide was initiated (for use in landslide inventory mapping). Our study shows that InSAR can be used to assist in the investigation of pre-failure slope conditions. For instance, our initial findings suggest there is more land motion prior to failure on clay soils with gentler slopes than on those with steeper slopes. A greater understanding of pre-failure slope conditions will support the generation of a more dependable susceptibility map. Our study also discusses the integration of InSAR deformation-rate maps and time-series analysis with rainfall data in support of the development of rainfall thresholds for different terrains. The information provided by InSAR can enhance inventory and susceptibility mapping, which will better assist with the island’s current disaster mitigation and resiliency efforts.

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