Pattern Analysis and Recognition in Remote Sensing

Dear Colleagues,

The recent advances in optics and photonics have stimulated the deployment of sensing devices of high spatio-temporal and spectral resolutions. These sensors are now placed on satellite, aerial, UAV and ground acquisition platforms used for material, object and terrain land detection and classification. A variety of research topics has been and continues to be at the focal point of remote sensing and pattern recognition researchers, such as: automatic identification of 2D and 3D patterns in unimodal and multimodal remote sensing data, including multi-scale aerial and satellite data, multi- and hyperspectral imagery, SAR-, radargrammetric and SAR-tomography data; recognition of temporal patterns in remote sensing data, e.g., image-based flow estimation and learning from InSAR data (traffic, glaciers, currents, etc.); identification of spatial patterns in remote sensing data exploiting 3D and 4D models (e.g. GIS, CAD).

Although high spatial and spectral resolution improves classification accuracy, it also imposes several research challenges derived as a consequence of the so-called “curse of dimensionality”; the difficulties arising when we need to analyze and organize data in high dimensional spaces. At the same time, the surge of increasingly complex and powerful machine learning techniques (including deep learning and tensor-based classifiers) has brought about a totally new landscape as regards the boundaries of pattern recognition methods. This Special Issue aims at exploring the potential of new ideas and technologies from the field of machine learning and pattern recognition in remote sensing applications and further investigate the overlap between remote sensing and computer vision/image analysis.

Prof. Nikolaos Doulamis
Guest Editor

Manuscript Submission Information

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