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2017, Journal of Space Technology
Automatic target detection in satellite images is a challenging problem due to the varying size, orientation and background of the target object. The traditionally engineered features such as HOG, Gabor feature and Hough transform do not work well for huge data of high resolution. Robust and computationally efficient systems are required which can learn presentations from the massive satellite imagery. In this paper, a target detection system for satellite imagery is proposed which uses EdgeBoxes and Convolutional Neural Network (CNN) for classifying target and non-target objects in a scene. The edge information of targets in satellite imagery contains very prominent and concise attributes. EdgeBoxes uses the edge information to filter the set of target proposals. CNN is a deep learning classifier with a high learning capacity and a capability of automatically learning optimum features from training data. Moreover, CNN is invariant to minor rotations and shifts in the target object. Encouraging experimental results have been obtained on a large dataset which shows the optimum performance and robustness of our system in complex scenes.
Automatic target detection in satellite images is a challenging problem due to the varying size, orientation and background of the target object. The traditionally engineered features such as HOG, Gabor feature and Hough transform do not work well for huge data of high resolution. Robust and computationally efficient systems are required which can learn presentations from the massive satellite imagery. In this paper, a target detection system for satellite imagery is proposed which uses EdgeBoxes and Convolutional Neural Network (CNN) for classifying target and non-target objects in a scene. The edge information of targets in satellite imagery contains very prominent and concise attributes. EdgeBoxes uses the edge information to filter the set of target proposals. CNN is a deep learning classifier with a high learning capacity and a capability of automatically learning optimum features from training data. Moreover, CNN is invariant to minor rotations and shifts in the target object. Encouraging experimental results have been obtained on a large dataset which shows the optimum performance and robustness of our system in complex scenes.
IOP Conference Series: Materials Science and Engineering
Automated vehicle detection in satellite images using deep learningInternational Journal of Engineering Research and Technology (IJERT)
IJERT-Detection and Classification of Objects in Satellite Images using Custom CNN2021 •
https://www.ijert.org/detection-and-classification-of-objects-in-satellite-images-using-custom-cnn https://www.ijert.org/research/detection-and-classification-of-objects-in-satellite-images-using-custom-cnn-IJERTV10IS060287.pdf Satellite image analysis is being increasingly used for many applications like surveillance, military, geo-spatial surveys and environmental impacts and change monitoring. Automatic detection and classification of objects is an important functionality of satellite image analysis. Due to the nature and size of objects and the varied visual features, it becomes challenging to detect and classify objects in aerial images. Manual detection of objects in these images is very timeconsuming due to the nature and that data captured in these images. It is desirable to automate the detection of various features or objects from these satellite images. The conventional methods for object classification involve two stages: (i) identify the regions with object presence in the image and (ii) classify the objects in the regions. Additionally, detection of objects becomes challenging in presence of complexities in background, size, noise, and distance parameters. This work proposes a customized convolutional neural network to detect and classify three different objects such as trees, building and cars in the images. It also aims to understand and outline briefly the performance characteristics of the considered custom CNN.
Remote Sensing
Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite ImagesObject detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object...
2020 •
An Object Detection is related to Computer Vision. Object detection enables detecting instances of objects in images and videos. It identifies the feature of Images rather than traditional object detection methods and generates an intelligent understanding of images just like human vision works. In this paper, We revived begins the brief introduction of deep learning and object detection framework like Convolutional Neural Network(CNN), Recurrent neural network (RNN), faster RNN, You only look once (YOLO). Then we focus on our proposed object detection architectures along with some modifications. The traditional model detects a small object in images. We have some modifications to the model. Our proposed method gives the correct result with accuracy.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Satellite Imagery Classification with Deep Learning : A SurveyObject detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution satellite images. In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus, the High-Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this research, a different technique for vehicle detection from the images obtained from high resolution sensors is reviewed. This review presents the recent progress in the field of object detection from satellite imagery using deep learning.
2021 •
Object detection is one of the fundamental objectives in Applied Computer Vision. In some of the applications, object detection becomes very challenging such as in the case of satellite image processing. Satellite image processing has remained the focus of researchers in domains of Precision Agriculture, Climate Change, Disaster Management, etc. Therefore, object detection in satellite imagery is one of the most researched problems in this domain. This paper focuses on aircraft detection. in satellite imagery using deep learning techniques. In this paper, we used YOLO deep learning framework for aircraft detection. This method uses satellite images collected by different sources as learning for the model to perform detection. Object detection in satellite images is mostly complex because objects have many variations, types, poses, sizes, complex and dense background. YOLO has some limitations for small size objects (less than∼32 pixels per object), therefore we upsample the predicti...
Maǧallaẗ al-abḥāṯ al-handasiyyaẗ
Comparison of deep learning models in terms of multiple object detection on satellite images2021 •
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Multi-class Object Detection Model in Satellite Images Using Convolutional Neural Network2021 •
2024 •
5th International Conference on Cloud and Internet of Things (ICCIoT 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Cloud and IoT. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Cloud computing and Internet of Things.
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