Analysis Of High Resolution Aerial Images For Object Detection

1989 ◽  
Author(s):  
Mohan M. Trivedi ◽  
Amol G. Bokil ◽  
Mourad B. Takla ◽  
George B. Maksymonko ◽  
J. Thomas Broach
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Aerial Images

scholarly journals Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3341 ◽  
Author(s):  
Hilal Tayara ◽  
Kil Chong
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Computation Time ◽  
Aerial Images ◽  
Feature Maps ◽  
Two Stage ◽  
One Stage ◽  
Wide Range ◽  
Feature Pyramid ◽  
Very High

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.

scholarly journals Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 166-179 ◽  
Author(s):  
Ziyang Tang ◽  
Xiang Liu ◽  
Hanlin Chen ◽  
Joseph Hupy ◽  
Baijian Yang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Computer Assisted ◽  
Visual Interpretation ◽  
Two Phase ◽  
Aerial Systems ◽  
Spot Fires

Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( 3840 × 2160 ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from 0 . 29 to 0 . 67 , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.

scholarly journals AUTOMATIC OBJECT EXTRACTION FROM HIGH RESOLUTION AERIAL IMAGERY WITH SIMPLE LINEAR ITERATIVE CLUSTERING AND CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 61-66 ◽  
Author(s):  
A. C. Carrilho ◽  
M. Galo
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Machine Learning Techniques ◽  
Object Extraction ◽  
Multi Scale ◽  
Learning Techniques ◽  
Simple Linear Iterative Clustering ◽  
Fast Region

<p><strong>Abstract.</strong> Recent advances in machine learning techniques for image classification have led to the development of robust approaches to both object detection and extraction. Traditional CNN architectures, such as LeNet, AlexNet and CaffeNet, usually use as input images of fixed sizes taken from objects and attempt to assign labels to those images. Another possible approach is the Fast Region-based CNN (or Fast R-CNN), which works by using two models: (i) a Region Proposal Network (RPN) which generates a set of potential Regions of Interest (RoI) in the image; and (ii) a traditional CNN which assigns labels to the proposed RoI. As an alternative, this study proposes an approach to automatic object extraction from aerial images similar to the Fast R-CNN architecture, the main difference being the use of the Simple Linear Iterative Clustering (SLIC) algorithm instead of an RPN to generate the RoI. The dataset used is composed of high-resolution aerial images and the following classes were considered: house, sport court, hangar, building, swimming pool, tree, and street/road. The proposed method can generate RoI with different sizes by running a multi-scale SLIC approach. The overall accuracy obtained for object detection was 89% and the major advantage is that the proposed method is capable of semantic segmentation by assigning a label to each selected RoI. Some of the problems encountered are related to object proximity, in which different instances appeared merged in the results.</p>

Using high‐resolution aerial imagery and deep learning to detect tree spatio-temporal dynamics at the treeline

2021 ◽  
Author(s):  
Mirela Beloiu ◽  
Dimitris Poursanidis ◽  
Samuel Hoffmann ◽  
Nektarios Chrysoulakis ◽  
Carl Beierkuhnlein
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Temporal Dynamics ◽  
Tree Density ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Remote Areas ◽  
Spatio Temporal ◽  
Study Sites

&lt;p&gt;Recent advances in deep learning techniques for object detection and the availability of high-resolution images facilitate the analysis of both temporal and spatial vegetation patterns in remote areas. High-resolution satellite imagery has been used successfully to detect trees in small areas with homogeneous rather than heterogeneous forests, in which single tree species have a strong contrast compared to their neighbors and landscape. However, no research to date has detected trees at the treeline in the remote and complex heterogeneous landscape of Greece using deep learning methods. We integrated high-resolution aerial images, climate data, and topographical characteristics to study the treeline dynamic over 70 years in the Samaria National Park on the Mediterranean island of Crete, Greece. We combined mapping techniques with deep learning approaches to detect and analyze spatio-temporal dynamics in treeline position and tree density. We use visual image interpretation to detect single trees on high-resolution aerial imagery from 1945, 2008, and 2015. Using the RGB aerial images from 2008 and 2015 we test a Convolution Neural Networks (CNN)-object detection approach (SSD) and a CNN-based segmentation technique (U-Net). Based on the mapping and deep learning approach, we have not detected a shift in treeline elevation over the last 70 years, despite warming, although tree density has increased. However, we show that CNN approach accurately detects and maps tree position and density at the treeline. We also reveal that the treeline elevation on Crete varies with topography. Treeline elevation decreases from the southern to the northern study sites. We explain these differences between study sites by the long-term interaction between topographical characteristics and meteorological factors. The study highlights the feasibility of using deep learning and high-resolution imagery as a promising technique for monitoring forests in remote areas.&lt;/p&gt;

Object Detection with an Improved Level Set Method

2013 ◽  
Vol 303-306 ◽  
pp. 1060-1066
Author(s):  
Hui Cao ◽  
Bo Cheng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Level Set ◽  
Region Of Interest ◽  
Remote Sensing Image ◽  
Aerial Images ◽  
Important Research ◽  
Speed Up ◽  
Level Set Formulation

Object detection is quite an important research in remote sensing image analysis. In this paper, we propose an edge and region based model for high resolution remote sensing image segmentation with level set formulation. Our method firstly made an image enhancement based on ROI (Region of Interest). By introducing the edge speed-up function, we can save time through decreasing the iterations and get a flexible segmentation considering the complexity of high resolution remote sensing image. Our method has been preliminarily applied to QuickBird and aerial images.

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

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.

Train in Dense and Test in Sparse: A Method for Sparse Object Detection in Aerial Images

2020 ◽  
pp. 1-5
Author(s):  
Kun Ding ◽  
Guojin He ◽  
Huxiang Gu ◽  
Zisha Zhong ◽  
Shiming Xiang ◽  
...  
Keyword(s):  
Object Detection ◽  
Aerial Images
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