Dear Colleagues,

With the recent advances in remote sensing technologies for Earth observation (EO), many different remote sensors now collect data with distinctive properties. EO data have been employed to monitor croplands and forested areas, oceans and seas, urban settlements, mountainous areas, climate-related processes, and natural hazards. The spectral, spatial, and temporal resolutions of remote sensors (e.g., optical, radar) have been continuously improving, making geospatial monitoring more accurate and comprehensive than ever before. Therefore, newly developed deep learning methods and machine learning techniques are allowing us to tackle problems that were considerably difficult to approach just a few years ago.

Nevertheless, many challenges still remain in the remote sensing field, which encourages new efforts and developments in order to better understand remote sensing images via image-processing techniques. Therefore, this Special Issue aims to present new machine and deep learning techniques within new application areas in remote sensing acquired from unmanned aerial vehicles (UAVs), aircraft, satellite platforms and different sensors (multispectral/hyperspectral optical, radar, lidar). Review papers on this topic are also welcome.

Therefore, authors are encouraged to submit articles on topics including but not limited to the following:

• Deep learning methods using remote sensing data;
• Multitemporal and multi-sensor data fusion and classification;
• Time-series image analysis;
• Agricultural and forest monitoring;
• SAR-based features;
• Optical-based features;
• Land-use and land-cover change classification;
• Usage of the analysis-ready image collections and cloud computing services;
• Geospatial data analysis for change detection.

Dr. Dino Dobrinić
Dr. Mateo Gašparović
Guest Editors

Manuscript Submission Information

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• deep learning methods using remote sensing data
• multitemporal and multi-sensor data fusion and classification
• time-series image analysis
• agricultural and forest monitoring
• SAR-based features
• optical-based features
• land-use and land-cover change classification
• usage of the analysis-ready image collections and cloud computing services
• geospatial data analysis for change detection

Title: Detection of Electromagnetic Seismic Precursors from Swarm Data by An Enhanced Martingale Analytics
Authors: Yaxin Bi; Shane Harrigan; MingJun Huang; Christopher Cillian O’Neill; Wei Zhai; Jianbao Sun; Xuemin Zhang
Affiliation: Ulster University
Abstract: The detection of seismic activity precursors as part of an alarm system will provide opportunities for minimization of the social and economic impact caused by earthquakes. It has long been envisaged and a growing body of empirical evidence suggest as much that the Earth’s electromagnetic field could contain precursors to seismic events. The ability to capture and monitor electromagnetic field activity has increased in the past years as more sensors and methodologies emerge. Missions such as Swarm have enabled researchers to access near-continuous observations of electromagnetic activity at second intervals allowing for more detailed and exciting studies. In this paper, we present an approach designed to detect precursor anomalies in electromagnetic field data from Swarm satellites and initial analysis results. This works towards developing a continuous and effective monitoring system of seismic activities based on SWARM measurements and tools. We develop an enhanced form a probabilistic model based on the Martingale probability theories that allow for testing the null hypothesis to indicate abnormal changes in electromagnetic field activity. We evaluate this enhanced approach in two experiments. Firstly, we perform a quantitative comparison on well-understood and popular benchmark datasets alongside the conventional approach. We find that the enhanced version produces more accurate anomaly detections overall. Secondly, we use three case studies of seismic activity (namely earthquakes in Mexico, Greece, and Croatia) to assess our approach and the results show that our method can detect anomalous phenomena in the electromagnetic data.

Title: Transformers deep learning model for remote sensing image analysis: status, challenges and applications
Authors: Ruikun Wang, Lei Ma, Guangjun He, Ming Chang, Ying Liang
Affiliation: Nanjing University
Abstract: Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of relative lack of review. To understand the trends of transformers in RS, we undertook a meta-analysis of the major research on transformers over the past two years by dividing the application of transformers into 8 domains: land use/ land cover (LULC) classification, segmentation, fusion, change detection, object detection, object recognition, registration, and others. Quantitative results show that transformers have advantages in LULC classification and fusion, with more stable performance on segmentation and object detection. Combining the analysis results on LULC classification and segmentation, we have found that transformers need more parameters to achieve higher accuracy. Additionally, further research is also needed regarding inference speed to improve transformers' performance. A series of works have revealed that the score of pre-training transformer-based models is higher than no pre-training in all evaluating indicators. Therefore, large RS models based on pre-training have been proposed recently as we expected. Another challenge arises with RS images which are different from images used in CV. This leads to some pre-processing and modules being required by transformers in RS. Subsequently, a survey of application scenarios of transformers in RS is conducted. It was determined that the most common application scenes for transformers in our database are urban, farmland, and water bodies. We also find that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development.