Object Detection from Satellite Images Employing Ostu-Entropy Segmentation and Deep Neural Networks
Keywords:
Satellite image detection, remote sensing, image enhancement, image denoising, deep neural networks, classification accuracyAbstract
Satellite images have extremely important applications in the domain of remote sensing, security, military, climate monitoring, disaster management etc. One of the key aspects of satellite image analysis happens to be object detection of satellite images which allows to access the context of the image and renders significant information. However, due to the enormous distance from which the image is captured, as well as noise and blurring effects, its is extremely difficult to attain high accuracy of object detection in case of satellite images. This paper presents a method to enhance the quality of satellite images through pre-processing prior to analysis using machine learning algorithms. Different noise categories affecting satellite images have been investigated and an iterative denoising approach has been developed for denoising. Further, a deep neural network model has been developed to identify objects from satellite images. It is shown that the proposed approach attains a classification accuracy of 92.243%, recall of 91.23%, specificity of 91.98%, precision of 90.943% and F-measure of 91.588% outperforming contemporary approaches in the domain.
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