Advanced Change Detection and Anomaly Detection in Remote Sensing

Dear Colleagues,

Change detection and anomaly detection are key tasks in the field of remote sensing, enabling effective monitoring of various dynamic changes on the Earth's surface, providing reliable information for decision-making, and meeting practical needs. With the continuous development of remote sensing technology, data from optical, SAR, LIDAR, multispectral imagery, and other sources are becoming increasingly widespread. Efficiently and accurately performing change detection and anomaly detection in these diverse datasets has become a major focus of current remote sensing research. This topic aims to highlight innovative algorithms and methods to improve the efficiency and accuracy of change and anomaly detection in remote sensing data.

Change detection identifies target differences by comparing remote sensing data from different times, while anomaly detection focuses on identifying data points or areas that deviate from normal patterns. Both play crucial roles in applications such as environmental monitoring, urban planning, disaster response, agricultural management, and natural resource exploration.

As the complexity and volume of remote sensing data increase, the development of new algorithms capable of processing high-dimensional, multi-source, and multi-temporal data has become increasingly urgent. At the same time, the integration of deep learning models and big data analytics offers new possibilities for improving detection accuracy and enabling automated processing.

This Special Issue aims to foster research and development in advanced change and anomaly detection methods for remote sensing. Topics of interest include, but are not limited to, the following:

• Algorithms for multi-source and multi-temporal remote sensing change detection.
• Deep learning approaches for anomaly detection in remote sensing images.
• Change detection in urban environments using remote sensing data.
• Anomaly detection in vegetation, land cover, and agricultural monitoring.
• Integration of LIDAR, SAR, and optical data for change and anomaly detection.
• Time-series analysis for remote sensing-based anomaly detection.
• Anomaly detection using hyperspectral and multispectral data.
• Methods for detecting natural disasters, such as floods, forest fires, and earthquakes, using remote sensing.
• Data fusion and feature extraction for improved anomaly detection.
• Remote sensing applications for monitoring environmental changes and urban growth.
• Cross-domain transfer learning for change and anomaly detection in remote sensing.
• Cloud-based platforms and tools for large-scale change detection and anomaly identification.

Dr. Ganchao Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• change detection algorithms
• anomaly detection techniques
• remote sensing time-series analysis
• multi-source data fusion urban change detection
• disaster monitoring using remote sensing
• vegetation and land cover change detection
• hyperspectral and sar data for anomaly detection
• deep learning for change and anomaly detection
• remote sensing in environmental monitoring
• anomaly detection in agricultural management
• cross-domain transfer learning in remote sensing