Computer Vision-Based Methods and Tools in Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 20709
Special Issue Editor
Interests: pattern recognition; computer vision; expert systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The progress of remote sensing imaging has been closely associated with computer vision and pattern recognition. Land cover mapping, target detection, change detection and boundary extraction, as well as pattern inference from time-series of imaging data, pose challenges for traditional image computer vision and pattern recognition tasks, such as image clustering, classification and segmentation. Engineered feature vectors have been applied for the analysis of optical, SAR, multispectral or hyperspectral images, as well as point clouds. Later, visual dictionaries, the so-called bag-of-visual-words models (BoVW), incorporated the statistics associated with each problem at hand. In the context of semantic segmentation, numerous approaches, including active contours, Markov random fields (MRF) and superpixels, have been combined with descriptors or BoVWs and applied on remote sensing data. The rapid evolution of powerful GPUs and the availability of large datasets aided extraordinary advances in deep-learning-based computer vision, starting from the performance breakthrough of AlexNet in ILSVRC 2012. This new paradigm reinvigorated the interest in computational tools for remote sensing. Numerous works are regularly published for the analysis of remote sensing data with deep neural networks. Convolutional neural networks (CNNs) and derivative architectures, such as VGG, ResNet and Inception, play a prominent role in this direction. Another deep learning branch in remote sensing consists of applications of recurrent neural networks (RNNs), including long short-term memory (LSTM) networks, on time-series of images.
This Special Issue aims to explore state-of-the-art computer vision and pattern recognition applications in remote sensing. Research contributions, including surveys, are welcome. In particular, novel contributions that cover, but are not limited to, the following application domains are welcome:
- Land cover mapping;
- Target detection;
- Change detection;
- Boundary extraction;
- Pattern analysis on time-series of imaging data;
- Works carried out at all scales and in all environments, including surveys and comparative studies, as well as the description of new methodologies, best practices, advantages and limitations for computational tools in remote sensing.
Prof. Dr. Michalis Savelonas
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.
Keywords
- land cover mapping
- target detection
- change detection
- point-clouds
- LiDAR
- SAR imaging
- multispectral imaging
- hyperspectral imaging
- computer vision
- pattern recognition
- deep learning
- machine learning
- image analysis
- 3D shape analysis
- time-series analysis
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