Improving the Accuracy of Segmentation of Remote Sensing Images using Deep Learning

Subject Areas : Majlesi Journal of Telecommunication Devices

Adel__Hamdy__Dhayef Adel__Hamdy__Dhayef ¹ , Mehran Emadi ^{2 *}

1 - Department of Computer Engineering, Isfahan (Khorasgan) branch, Islamic Azad University Isfahan, Iran
2 - Department of Electrical Engineering, Mobarakeh Branch, Mobarakeh, Isfahan, Iran

Received: 2024-07-31 Accepted : 2024-11-10 Published : 2025-01-29

Keywords: Segmentation, Hyper Spectral Images, Remote Sensing, Morphological Operators, Convolution Neural Networks,

Abstract :

Image segmentation is used to exploit remote sensing images with high resolution. The purpose of segmentation is to create different segments with common features. For example, residential areas, forests, rivers and other areas are obtained in subdivision. But the random position of different areas on the ground has caused the accuracy of segmentation methods to be low. Using deep learning methods can improve segmentation accuracy. In this paper, a SegNet convolution deep neural network is proposed for segmentation of high resolution (HR) remote sensing images. The proposed strategy is to improve the semantic segmentation performance of images. The proposed SegNets strategy is carried out in two steps. The proposed method has been evaluated with accuracy criteria and F1 score. The results show that the accuracy is improved by more than 4% compared to other methods based on deep learning. Also, other evaluation criteria such as ROC have been used. The results of this criterion also show the superiority of this proposed method.