Special Issue "Data Mining and Feature Extraction from Satellite Images and Point Cloud Data"
A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).
Deadline for manuscript submissions: closed (31 March 2019)
Dr. Pedram Ghamisi
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Division Exploration, Machine Learning Group, Germany
Website | E-Mail
Interests: spectral and spatial techniques for hyperspectral image classification; multisensor data fusion; machine learning; deep learning
The vibrant field of Earth observation (EO), or remote sensing, is now facing an entirely different dimension of challenge in image interpretation due to the tremendous volumes and huge variety of data being generated by EO missions. An enormous increase in the number of missions coupled with a wide variety of available sensors (e.g., radar, passive microwave, thermal and LiDAR) have led the community to an unprecedented number and complexity of data to process, which is already a major challenge to the existing algorithms.
Such an increase in dimensionality, volume, and varieties provide users with rich data contain for a plethora of applications. However, for a specific application, not all the measurements are important and useful. This data contain may cause a serious issue known as “curse of dimensionality”, which negatively influences on the corresponding feature space for representing the data and downgrades the quality of the further processing steps such as data classification. To address this issue, data mining, which includes feature generation, feature selection, and feature extraction, is a crucial step.
It is expected that the advancement of data mining will continue to push the remote sensing and photogrammetry communities forward. Hence, we passionately encourage authors to submit original research articles, case studies, and review papers from both theoretical and application-oriented perspectives on this important and vibrant subject. In more details, topics appropriate for this Special Issue include (but are not necessarily limited to):
- Dimensionality reduction
- Feature selection, extraction, and object tracking
- Deep learning
- Spectral, spatial, and elevation information extraction
- Feature fusion
- Low-rank models for classification, detection, unmixing, resolution enhancement, and denoising.
Dr. Pedram Ghamisi
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 papers will be 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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1000 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.
- dimensionality reduction
- feature selection, feature extraction, and object tracking
- deep learning
- spectral, spatial, and elevation information extraction
- feature fusion
- low-rank models for classification, detection, unmixing, resolution enhancement, and denoising