Content area
Both remote sensing and medical fields benefited a lot from the machine learning methods, originally developed for computer vision and multimedia. We investigate the applicability of the same data mining-based machine learning (ML) techniques for exploring the structure of both Earth observation (EO) and medical image data. Support Vector Machine (SVM) is an explainable active learning tool to discover the semantic relations between the EO image content classes, extending this technique further to medical images of various types. The EO image dataset was acquired by multispectral and radar sensors (WorldView-2, Sentinel-2, TerraSAR-X, Sentinel-1, RADARSAT-2, and Gaofen-3) from four different urban areas. In addition, medical images were acquired by camera, microscope, and computed tomography (CT). The methodology has been tested by several experts, and the semantic classification results were checked by either comparing them with reference data or through the feedback given by these experts in the field. The accuracy of the results amounts to 95% for the satellite images and 85% for the medical images. This study opens the pathway to correlate the information extracted from the EO images (e.g., quality-of-life-related environmental data) with that extracted from medical images (e.g., medical imaging disease phenotypes) to obtain geographically refined results in epidemiology.
Details
Metadata;
Datasets;
Radarsat;
Data mining;
Satellite imagery;
Medical imaging;
Labeling;
Remote sensing;
Computer vision;
Radar imaging;
Machine learning;
Feedback;
Big Data;
Semantics;
Support vector machines;
Computed tomography;
Graph representations;
Sensors;
Classification;
Public health;
Archives & records;
Image acquisition;
Image quality;
Satellites
; Buiu Octavian 4
; Serban, Andreea Iren 5 1 Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, 050095 Bucharest, Romania; [email protected], Department of Oncology, Carol Davila University of Medicine and Pharmacy, 252 Siseaua Fundeni, 022328 Bucharest, Romania, National Institute for Research and Development in Microtechnologies—IMT Bucharest, 126A Erou Iancu Nicolae Street, 077190 Voluntari, Romania; [email protected] (A.D.); [email protected] (F.A.); [email protected] (R.P.); [email protected] (O.B.)
2 Remote Sensing Technology Institute, German Aerospace Center, Münchener Str. 20, 82234 Wessling, Germany
3 National Institute for Research and Development in Microtechnologies—IMT Bucharest, 126A Erou Iancu Nicolae Street, 077190 Voluntari, Romania; [email protected] (A.D.); [email protected] (F.A.); [email protected] (R.P.); [email protected] (O.B.), Department of Automatic Control and Systems Engineering, National University of Science and Technology Politehnica Bucharest, 313 Splaiul Independenței, 060042 Bucharest, Romania
4 National Institute for Research and Development in Microtechnologies—IMT Bucharest, 126A Erou Iancu Nicolae Street, 077190 Voluntari, Romania; [email protected] (A.D.); [email protected] (F.A.); [email protected] (R.P.); [email protected] (O.B.)
5 Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, 050095 Bucharest, Romania; [email protected], Department of Preclinical Sciences, Faculty of Veterinary Medicine, University of Agronomic Sciences and Veterinary Medicine, 105 Splaiul Independenței, 050095 Bucharest, Romania