Urban Multi-Category Object Detection Using Aerial Images
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".
Deadline for manuscript submissions: closed (1 July 2022) | Viewed by 30397
Special Issue Editor
Interests: Machine Learning, Data Compression, Data Mining, Optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The detection of urban objects from aerial images has become a prevalent and useful task, as aerial images may be used for surveillance, tracking, mapping, or search and rescue tasks. However, satellite and aerial image prices have significantly decreased in the past few years. Due to the availability of UAVs and drones for real-time monitoring of a defined area or exploratory flights, a precise detection of the captured objects is required, as many kinds of objects may be present simultaneously in a picture and must be detected and classified.
Many approaches that utilize deep neural networks have been developed recently. Many so-called standard algorithms based on convolutions and residual or recurrent networks have been modified to fulfill the task. Nevertheless, new architectures that can deal with noisy images, complex backgrounds, and complex environments are still required. A transformer approach that has become immensely popular in text processing is also auspicious in image processing, and it may lead to revolutionary results. Moreover, tracking objects in a set of consecutive images needs modified algorithms with memory and partial or complete cover of other objects due to the camera-resistant approaches’ movement. However, everything starts with the excellent image preprocessing phase, dealing with different light conditions, day- and night-time, weather conditions, and other aspects.
Any high-quality novel and efficient approaches that deal with all or any aspect of urban multi-object detection from the aerial images are welcome.
Dr. Jan Platoš
Guest Editor
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Keywords
- Aerial images
- Urban object classification
- Aerial image preprocessing
- Object detection
- Aerial image enhancement
- Multi-object localization
- Multi-object detection and classification
- Weather condition resistance
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