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Exploiting Arma 3 to Construct Synthetic Data for Military Target Detection on Remote Sensing Imagery
Yechan Kim, JongHyun Park, SooYeon Kim, Sihyun Kim, Sung Heon Kim, YeongMin Ko, Junggyun Oh, Dongho Yoon, Moongu Jeon
http://doi.org/10.5626/JOK.2025.52.1.9
Recently, satellite-based surveillance and reconnaissance systems have garnered significant attention in the military sector. However, the acquisition of large-scale satellite imagery for training military target detection models presents practical challenges, primarily due to high costs and security concerns. To tackle this issue, this paper proposes an algorithm for generating synthetic satellite imagery and annotations for military target detection using Arma 3, a well-known military simulation game. Arma 3 offers realistic military equipment and environments, which facilitates the creation of high-quality synthetic data. This study specifically validates the proposed method by demonstrating that our synthetic dataset can effectively complement real-world data, utilizing the DOTA dataset and web-scraped military images.
Extreme Environment Rotated Object Detection Network
Giljun Lee, Junyaup Kim, Gwanghan Lee, Simon S. Woo
http://doi.org/10.5626/JOK.2023.50.11.966
With the advancement of object detection models, it is possible to efficiently infer synthetic aperture radar (SAR) and electro-optical (EO) satellite images. However, conventional object detection models using horizontal bounding boxes (HBB) struggle to detect small and densely grouped objects in satellite images. To address this issue, this paper proposes E^2RDet. This algorithm effectively modifies the structure of the Yolov7 object detection model, enabling it to accurately detect objects represented by oriented bounding boxes (OBB) in SAR images. This algorithm improves the object detection model architecture and loss function to facilitate learning of an object"s dynamic (orientation) posture. Using various training datasets, E^2RDet demonstrates performance improvements across three benchmark SAR datasets. This indicates that existing HBB object detection models can train and perform object detection on objects represented by OBBs.
DPESS: Daytime Satellite Imagery-based Prediction of Demographic Attributes Using Embedding Spatial Statistics
Hyunji Cha, Sungwon Han, Donghyun Ahn, Sungwon Park, Meeyoung Cha
http://doi.org/10.5626/JOK.2020.47.8.742
Studies are being actively conducted to predict or analyze demographics used as socioeconomic factors using satellite images. We present a new approach, called DPESS, for estimating demographic attributes from daytime satellite imagery based on a deep neural network model. The four steps of the DPESS summarize any number of input images into a fixed-length embedded vector without a considerable loss of information, which is possible because of its unique structure and technique like transfer learning and embedded spatial statistics. Our extensive validation demonstrates that the DPESS model can predict various advanced demographics such as population density (R² =0.94), population count by age group (0.80), population count by education degree (0.79), and total purchase amount per household (0.80). We discuss future applications of this method in terms of applying our algorithm to other countries.
Ship Detection using CNN based on Contrast Fusion Technique in Satellite Images : Accuracy Enhancement
Sunggyun Im, Youngbae Jeon, Junghwan Hwang, Jiwon Yoon
http://doi.org/10.5626/JOK.2019.46.8.823
The satellite has various missions such as ground/marine observation, communication, broadcasting, etc. Satellite photographs provide information for the maintenance of marine security and traffic control for ship detection. Since satellite photos are taken all over the earth, the memory storage is not sufficient to hold such data with each data being of a high resolution and requiring automatic ship detection using the computer. The existing literature on ship detection employed several deep learning models. However, the problem of processing speed due to the characteristics of satellite photographs leads to the necessity of using a CNN(Convolution Neural Network) model that has a comparably high processing speed. On the contrary, it is difficult to improve the accuracy and performance mostly due to factors such as marina, lighthouses and waves. Therefore, in this paper, we propose a model that improves the accuracy and performance by combining image contrast enhancement with the existing CNN. In addition, we have employed the overlap and rotation functions to increase the amount of data required for ship classification in the learning stage and implement automation detection technology considering window sliding to reduce detection speed in real satellite photographs. Also, the identified ship data has been used as learning data to improve accuracy for the model that can be used in the real industry.
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