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.

scholarly journals Vehicle Detection and Classification from High Resolution Satellite Images

2014 ◽  
Vol II-1 ◽  
pp. 1-8 ◽  
Author(s):  
L. Abraham ◽  
M. Sasikumar
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Weather Forecasting ◽  
Vehicle Detection ◽  
Satellite Imaging ◽  
Suitable Alternative ◽  
High Resolution Satellite Images ◽  
Technological Developments ◽  
High Resolution Satellite Imagery

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 work, a novel technique for vehicle detection from the images obtained from high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, difficult to distinguish from the background. But we are able to obtain a detection rate not less than 0.9. Thereafter we classify the detected vehicles into cars and trucks and find the count of them.

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 Adaptive digital technique for discriminating between shadow and water bodies in the high resolution satellite imagery

2019 ◽  
Vol 14 (31) ◽  
pp. 81-88
Author(s):  
Anaam Kadhim Hadi
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Google Earth ◽  
Experimental Results ◽  
Water Bodies ◽  
Digital Technique ◽  
Matlab Program ◽  
High Resolution Satellite Images ◽  
High Resolution Satellite Imagery

This research presents a new algorithm for classification theshadow and water bodies for high-resolution satellite images (4-meter) of Baghdad city, have been modulated the equations of thecolor space components C1-C2-C3. Have been using the color spacecomponent C3 (blue) for discriminating the shadow, and has beenused C1 (red) to detect the water bodies (river). The new techniquewas successfully tested on many images of the Google earth andIkonos. Experimental results show that this algorithm effective todetect all the types of the shadows with color, and also detects thewater bodies in another color. The benefit of this new technique todiscriminate between the shadows and water in fast Matlab program.

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