Semantic Segmentation of Satellite Images using Deep Learning
Chandra Pal Kushwah1, Kuruna Markam2
1Chandra Pal Kushwah*, Department of Electronics Engineering, Madhav Institute of Technology & Science, Gwalior (MP), India.
2Kuruna Markam, Department of Electronics Engineering, Madhav Institute of Technology & Science, Gwalior (MP), India.
Manuscript received on June 7, 2021. | Revised Manuscript received on June 10, 2021. | Manuscript published on June 30, 2021. | PP: 33-37 | Volume-10, Issue-8, June 2021 | Retrieval Number: 100.1/ijitee.H91860610821 | DOI: 10.35940/ijitee.H9186.0610821
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Bidirectional in recent years, Deep learning performance in natural scene image processing has improved its use in remote sensing image analysis. In this paper, we used the semantic segmentation of remote sensing images for deep neural networks (DNN). To make it ideal for multi-target semantic segmentation of remote sensing image systems, we boost the Seg Net encoder-decoder CNN structures with index pooling & U-net. The findings reveal that the segmentation of various objects has its benefits and drawbacks for both models. Furthermore, we provide an integrated algorithm that incorporates two models. The test results indicate that the integrated algorithm proposed will take advantage of all multi-target segmentation models and obtain improved segmentation relative to two models. Keywords: A Satellite Image, Deep Neural Network, U-net, SigNet.
Keywords: A Satellite Image, Deep Neural Network, U-net, SigNet.