3D-MODELING INFRASTRUCTURE FACILITIES USING DEEP LEARNING BASED ON HIGH RESOLUTION SATELLITE IMAGES

2020 ◽  
Author(s):  
Oleg Gvozdev ◽  
Natalya Kosheleva ◽  
Vladimir Kozub ◽  
Alexander Murynin ◽  
Andrey Richter
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
3D Modeling ◽  
Satellite Images ◽  
High Resolution Satellite Images

scholarly journals Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images

2020 ◽  
Vol 12 (3) ◽  
pp. 458 ◽  
Author(s):  
Ugur Alganci ◽  
Mehmet Soydas ◽  
Elif Sertel
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Satellite Images ◽  
Training Data ◽  
Object Localization ◽  
Detection Accuracy ◽  
Single Shot ◽  
High Resolution Satellite Images ◽  
Very High

Object 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 in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy.

scholarly journals Satellite Imagery Classification with Deep Learning : A Survey

2020 ◽  
pp. 36-46
Author(s):  
Niharika Goswami ◽  
Keyurkumar Kathiriya ◽  
Santosh Yadav ◽  
Janki Bhatt ◽  
Sheshang Degadwala
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Weather Forecasting ◽  
Vehicle Detection ◽  
Suitable Alternative ◽  
High Resolution Satellite Images ◽  
Technological Developments

Object 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.

Patch-Wise Semantic Segmentation of Sedimentation from High-Resolution Satellite Images Using Deep Learning

2021 ◽  
pp. 498-509
Author(s):  
Tahmid Hasan Pranto ◽  
Abdulla All Noman ◽  
Asaduzzaman Noor ◽  
Ummeh Habiba Deepty ◽  
Rashedur M. Rahman
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Satellite Images ◽  
Semantic Segmentation ◽  
High Resolution Satellite Images

Semantic segmentation of high-resolution satellite images using deep learning

2021 ◽  
Author(s):  
Kuldeep Chaurasia ◽  
Rijul Nandy ◽  
Omkar Pawar ◽  
Ravi Ranjan Singh ◽  
Meghana Ahire
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Satellite Images ◽  
Semantic Segmentation ◽  
High Resolution Satellite Images

scholarly journals SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images

2021 ◽  
Vol 7 ◽  
pp. e772
Author(s):  
Ahmed I. Shahin ◽  
Sultan Almotairi
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Visual Attention ◽  
Satellite Images ◽  
Boundary Detection ◽  
Single Shot ◽  
Building Detection ◽  
Desert Environment ◽  
Low Contrast ◽  
High Resolution Satellite Images

Building detection in high-resolution satellite images has received great attention, as it is important to increase the accuracy of urban planning. The building boundary detection in the desert environment is a real challenge due to the nature of low contrast images in the desert environment. The traditional computer vision algorithms for building boundary detection lack scalability, robustness, and accuracy. On the other hand, deep learning detection algorithms have not been applied to such low contrast satellite images. So, there is a real need to employ deep learning algorithms for building detection tasks in low contrast high-resolution images. In this paper, we propose a novel building detection method based on a single-shot multi-box (SSD) detector. We develop the state-of-the-art SSD detection algorithm based on three approaches. First, we propose data-augmentation techniques to overcome the low contrast images’ appearance. Second, we develop the SSD backbone using a novel saliency visual attention mechanism. Moreover, we investigate several pre-trained networks performance and several fusion functions to increase the performance of the SSD backbone. The third approach is based on optimizing the anchor-boxes sizes which are used in the detection stage to increase the performance of the SSD head. During our experiments, we have prepared a new dataset for buildings inside Riyadh City, Saudi Arabia that consists of 3878 buildings. We have compared our proposed approach vs other approaches in the literature. The proposed system has achieved the highest average precision, recall, F1-score, and IOU performance. Our proposed method has achieved a fast average prediction time with the lowest variance for our testing set. Our experimental results are very promising and can be generalized to other object detection tasks in low contrast images.

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