Open Access

ARTICLE

ResCD-FCN: Semantic Scene Change Detection Using Deep Neural Networks

S. Eliza Femi Sherley^1,*, J. M. Karthikeyan¹, N. Bharath Raj¹, R. Prabakaran², A. Abinaya¹, S. V. V. Lakshmi³

1 Department of Information Technology, Anna University, MIT Campus, Chennai, 600044, India
2 Computer Center, Anna University, MIT Campus, Chennai, 600044, India
3 Department of Computer Science and Engineering, Anna University, CEG Campus, Chennai, 600025, India

* Corresponding Author: S. Eliza Femi Sherley. Email:

Journal on Artificial Intelligence 2022, 4(4), 215-227. https://doi.org/10.32604/jai.2022.034931

Received 01 August 2022; Accepted 23 September 2022; Issue published 25 May 2023

Abstract

Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic (labels/categories) details before and after the timelines are analyzed. Periodical land change analysis is used for many real time applications for valuation purposes. Majority of the research works are focused on Convolutional Neural Networks (CNN) which tries to analyze changes alone. Semantic information of changes appears to be missing, there by absence of communication between the different semantic timelines and changes detected over the region happens. To overcome this limitation, a CNN network is proposed incorporating the Resnet-34 pre-trained model on Fully Convolutional Network (FCN) blocks for exploring the temporal data of satellite images in different timelines and change map between these two timelines are analyzed. Further this model achieves better results by analyzing the semantic information between the timelines and based on localized information collected from skip connections which help in generating a better change map with the categories that might have changed over a land area across timelines. Proposed model effectively examines the semantic changes such as from-to changes on land over time period. The experimental results on SECOND (Semantic Change detectiON Dataset) indicates that the proposed model yields notable improvement in performance when it is compared with the existing approaches and this also improves the semantic segmentation task on images over different timelines and the changed areas of land area across timelines.

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Keywords

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