Techniques and Applications of Remote Sensing, Synthetic Aperture Radar (SAR), and Optical Imaging in Diverse Domains

Dear Colleagues,

The advancements in remote sensing, Synthetic Aperture Radar (SAR), and optical imaging technologies have sparked a revolution in how we observe and analyze the Earth's surface and beyond. This progress has opened new horizons and presented unparalleled opportunities for understanding our planet and addressing a myriad of challenges. In this Special Issue, we aim to highlight the cutting-edge techniques and multifaceted applications of these technologies. By harnessing the potential of remote sensing, SAR, and optical imaging, researchers have significantly enhanced their capabilities to explore the Earth's landscapes, monitor environmental changes, and support various sectors with valuable insights.

From SAR's ability to penetrate clouds and provide all-weather imaging to optical spaceborne sensors capturing high-resolution imagery, these technologies have enabled us to study land use patterns, assess natural disasters, analyze agricultural productivity, and map urban development. Additionally, the integration of airborne imaging systems has further expanded the scope of application, offering dynamic perspectives and precise data collection a range of scenarios.

We encourage contributions that is through topics that include, but are not limited to, the following:

• Advances in remote sensing platforms, sensors, and imaging techniques.
• Image processing techniques for remote sensing data, including noise removal and artifact correction.
• Super-resolution imaging techniques for enhancing spatial details in remote sensing.
• Fusion of SAR and optical remote sensing data for improved analysis and interpretation.
• Applications of remote sensing in environmental monitoring, management, and climate change studies.
• Land cover and land use classification using remote sensing data, incorporating deep learning approaches.
• Object detection and recognition in SAR and optical images through deep learning techniques.
• Semantic segmentation of remote sensing imagery using convolutional neural networks (CNN) and other advanced AI.
• Change detection in SAR and optical time series data using deep learning methods.
• Transfer learning and domain adaptation for improved analysis of remote sensing data.
• Hyperspectral image classification and analysis using deep learning models.
• Image fusion techniques for integrating SAR and optical data in remote sensing applications.
• Unsupervised feature extraction methods using deep learning for remote sensing data analysis.
• Deep learning-based 3D reconstruction and modeling using airborne and spaceborne imagery.
• Radiometric and atmospheric correction techniques for accurate interpretation of remote sensing imagery.
• Geometric correction and registration methods for precise alignment of remote sensing data.
• Data compression and storage techniques for efficient management of remote sensing datasets.
• Preprocessing methods for seamless image mosaicking and stitching of remote sensing data.
• Quality assessment and validation of preprocessed remote sensing data.
• Data interpolation and gap-filling techniques for handling incomplete remote sensing datasets.

Dr. Armin Moghimi
Guest Editor

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