{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:12:32Z","timestamp":1775578352125,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing technology has been modernized by artificial intelligence, which has made it possible for deep learning algorithms to extract useful information from images. However, overfitting and lack of uncertainty quantification, high-resolution images, information loss in traditional feature extraction, and background information retrieval for detected objects limit the use of deep learning models in various remote sensing applications. This paper proposes a Bayes by backpropagation (BBB)-based system for scene-driven identification and information retrieval in order to overcome the above-mentioned problems. We present the Bayes R-CNN, a two-stage object detection technique to reduce overfitting while also quantifying uncertainty for each object recognized within a given image. To extract features more successfully, we replace the traditional feature extraction model with our novel Multi-Resolution Extraction Network (MRENet) model. We propose the multi-level feature fusion module (MLFFM) in the inner lateral connection and a Bayesian Distributed Lightweight Attention Module (BDLAM) to reduce information loss in the feature pyramid network (FPN). In addition, our system incorporates a Bayesian image super-resolution model which enhances the quality of the image to improve the prediction accuracy of the Bayes R-CNN. Notably, MRENet is used to classify the background of the detected objects to provide detailed interpretation of the object. Our proposed system is comprehensively trained and assessed utilizing the state-of-the-art DIOR and HRSC2016 datasets. The results demonstrate our system\u2019s ability to detect and retrieve information from remote sensing scene images.<\/jats:p>","DOI":"10.3390\/rs16132405","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T08:17:29Z","timestamp":1719821849000},"page":"2405","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Bayes R-CNN: An Uncertainty-Aware Bayesian Approach to Object Detection in Remote Sensing Imagery for Enhanced Scene Interpretation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4891-0233","authenticated-orcid":false,"given":"Sagar A. S. M.","family":"Sharifuzzaman","sequence":"first","affiliation":[{"name":"Department of AI and Robotics, Sejong University, Seoul 05006, Republic of Korea"}]},{"given":"Jawad","family":"Tanveer","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"}]},{"given":"Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China"}]},{"given":"Jun Hoong","family":"Chan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing 100871, China"}]},{"given":"Hyung Seok","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of AI and Robotics, Sejong University, Seoul 05006, Republic of Korea"}]},{"given":"Karam Dad","family":"Kallu","sequence":"additional","affiliation":[{"name":"Department of Robotics and Artificial Intelligence (R&AI), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad H-12, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-2956","authenticated-orcid":false,"given":"Shahzad","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Hanyang University, Seoul 04763, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/j.dt.2019.11.014","article-title":"Arbitrary-Oriented Target Detection in Large Scene SAR Images","volume":"16","author":"Han","year":"2020","journal-title":"Defence Technol."},{"key":"ref_2","first-page":"4407416","article-title":"A New Spatial-Oriented Object Detection Framework for Remote Sensing Images","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. 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