The field of remote sensing image analysis is constantly evolving. However, processing high-resolution images and comprehending the black boxes in land surface analysis and object recognition poses significant challenges. The need for a deeper exploration of these areas has become more pressing due to climate change, global security concerns, and border monitoring issues. With the surge in demand for satellite image analysis and advancements in deep learning techniques and remote sensing technologies, it has become necessary to have a comprehensive guide to navigate these complexities.

Deep Cognitive Modelling in Remote Sensing Image Processing is a groundbreaking solution to these challenges. This book delves into the depths of deep learning techniques and cognitive modeling to offer insights and solutions for optimizing existing models while simplifying the processing of high-resolution remote sensing images. By focusing on deep cognitive modeling, the book provides a framework for understanding and addressing the black boxes in land surface analysis and object recognition, empowering researchers and professionals to make meaningful advancements in the field.

This book, tailored for professionals and researchers in computer sciences, remote sensing, and related fields, explores cognitive algorithms, mathematical modeling, object localization, image segmentation, machine learning, and profound learning advancements. Through a collection of research articles and case studies, this book equips readers with the knowledge and tools needed to navigate and innovate in remote sensing image analysis, making it an indispensable resource in the era of rapidly advancing technology and increasing demands for satellite image analysis.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Advancements in Machine Learning and Deep Learning
Border Monitoring Remote Sensing
Cognitive Algorithm
Cognitive Approach in Remote Sensing Big Data
Cognitive Modelling Approach for Remote Sensing Data
Deep Learning in Remote Sensing
Georeferencing
Geospatial Data Analysis
Image Processing
Image Segmentation
Large-Scale and Multi-Resolution Image Analysis
Mathematical Modeling
Multi-Temporal Scene Classification
Neural Networks for Moving Objects
Object Localization
Scene Detection

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Editor/Author Biographies

Sadique Ahmad (Member, IEEE) received the Ph.D. degree from the Department of Computer Sciences and Technology, Beijing Institute of Technology, China, in 2019, and the master’s degree from the Department of Computer Sciences, IMSciences University, Peshawar, Pakistan, in 2015. Currently, he is associated with Prince Sultan University, Riyadh, Saudi Arabia as a Researcher. He has many collaborative scientific activities with international teams in different research projects in the following international universities. He has authored more than 67 research articles, which are published in peer-reviewed journals, books and conferences, including top journals, such as Information Sciences, Science China Information Sciences, Computational Intelligence and Neuroscience, Physica-A, and IEEE Access. Currently, he is focusing on Deep Cognitive Modeling for Trust Management in Social Cybersecurity, IoT, and Blockchain technologies. Previously, he worked on Cognitive Computing, Deep Cognitive Modeling for Students' Performance Prediction, and Cognitive Modeling in Object Detection using remote sensing images.

Dr. Muhammad Shahid Anwar is currently working as an Assistant Professor in the Department of AI & Software at Gachon University, Seongnam, South Korea. He received his Ph.D. degree in Information and Communication Engineering from the School of Information and Electronics, Beijing Institute of Technology, Beijing, China in 2021 and his M.Sc. in Telecommunications Technology from Aston University, Birmingham, U.K., in 2012. Dr. Shahid has authored and co-authored more than 50 publications including IEEE, Springer, IET, Hindawi, MDPI, Frontiers journals, and flagship conference papers. He has been honored with the “Outstanding Scholar of the Year 2020 Award" from the CSC Scholarship Council under the Ministry of Education China. Dr. Shahid also received the “Excellent Student of the Year 2020 Award" from the Beijing Institute of Technology, China. He has been serving as an editorial board member and guest editor in several Journals and a reviewer of quite a few Journals including ACM and IEEE Transactions. His research interests include 360-degree videos, immersive Media (Virtual Reality, AR), Cognitive Computing, Object Detection, Metaverse, and Quality of Experience (QoE) evaluations of VR telemedicine and healthcare systems. He is focusing on deep learning-based VR video evaluations and developed several Machine Learning based QoE Prediction models.

Dr. Ala Saleh D. Alluhaidan received the B.Sc. degree in computer science from Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, the M.Sc. degree in computer information systems from Grand Valley State University, MI, USA, and the Ph.D. degree in information systems and technology from Claremont Graduate University, CA, USA. She is currently an Associate Professor with the Department of Information Systems, Princess Nourah bint Abdulrahman University. Her current research interests include big data analytics, Cognitive Computing, health informatics, and Artificial Intelligence. Also, she achieved 48 research articles in peer-reviewed journals and conferences. Few research articles are under review in different prestigious journals.

Mohammed A. El-Affendi is currently a Professor of computer science with the Department of Computer Science, Prince Sultan University, the Former Dean of CCIS, AIDE, the Rector, the Founder, and the Director of the Data Science Laboratory (EIAS), and the Founder and the Director of the Center of Excellence in CyberSecurity. His current research interests include data science, intelligent and cognitive systems, machine learning, and natural language processing.

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