Open AccessSemantic Segmentation of Satellite Images using Deep Learning
Author(s) -
Chandra Pal Kushwah,
Kuruna Markam
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.h9186.0610821
Subject(s) - computer science , artificial intelligence , segmentation , pooling , deep learning , image segmentation , artificial neural network , segmentation based object categorization , scale space segmentation , convolutional neural network , pattern recognition (psychology) , encoder , computer vision , operating system
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.