[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Learning from class-imbalanced data

/pdf/remote-sensing-of-the-environment-4yfxjaaswe.pdf

Remote sensing of the environment

Pattern recognition

Antenna Theory And Design

Oil spill detection by satellite remote sensing

We present algorithms for automatic detection of oil spills in synthetic aperture radar (SAR) images. The algorithms consist of three main parts, namely: 1) detection of dark spots; 2) feature extraction from the dark spot candidates; and 3) classification of dark spots as oil spills or look-alikes. The algorithms have been trained on a large number of Radarsat and Envisat Advanced Synthetic Aperture Radar (ASAR) images. The performance of the algorithm is compared to manual and semiautomatic approaches in a benchmark study using 59 Radarsat and Envisat images. The algorithms can be considered to be a good alternative to manual inspection when large ocean areas are to be inspected

Oil Spill Detection in Radarsat and Envisat SAR Images

In this paper, we study surface slick characterization in polarimetric C-band synthetic aperture radar (SAR) data. The objective is to identify the most powerful multipolarization SAR descriptors for mineral oil spill versus biogenic slick discrimination. A systematic comparison of eight well-known multipolarization features is provided. The analysis is performed on data that we collected during a large-scale oil spill exercise at the Frigg field situated northwest of Stavanger, in June 2011. Controlled oil spills and simulated look-alikes were simultaneously captured within fine quad-polarization Radarsat-2 acquisitions during this experiment. Multipolarization features derived from only the copolarized complex scattering coefficients are explored. We find that the two most powerful multipolarization features extracted from this data set are the geometric intensity, measuring the combined intensity based on the determinant of the coherency matrix, and the real part of the copolarization cross product, which is related to the scattering behavior of the target. We show that these two features can distinguish between the simulated biogenic slicks and mineral oil types such as Balder and Oseberg blend, and that the discriminative power seems to be persistent with time.

Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features

A new and robust constant false alarm rate (CFAR) detector based on truncated statistics (TSs) is proposed for ship detection in single-look intensity and multilook intensity synthetic aperture radar data. The approach is aimed at high-target-density situations such as busy shipping lines and crowded harbors, where the background statistics are estimated from potentially contaminated sea clutter samples. The CFAR detector uses truncation to exclude possible statistically interfering outliers and TSs to model the remaining background samples. The derived truncated statistic CFAR (TS-CFAR) algorithm does not require prior knowledge of the interfering targets. The TS-CFAR detector provides accurate background clutter modeling, a stable false alarm regulation property, and improved detection performance in high-target-density situations.

Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations

In this paper, we compare satellite-borne Cand X-band synthetic aperture radar (SAR) data for marine oil spill observation. During large-scale oil-on-water exercises in the North Sea, quad-polarization Radarsat-2 (C-band) and dual-polarization TerraSAR-X (X-band) data were acquired with temporal distances of less than 24 min. The objective is to characterize and quantify differences in the Radarsat-2 and TerraSAR-X measurements. Three scene pairs are compared in terms of data quality and signal characteristics, including statistical properties and selected multipolarization (HH, VV) parameters. The signal characteristics are also compared among low-backscatter features of various origin within the individual pairs. No viable argument for selecting one sensor above the other is identified in the data quality study. In the statistical analysis, investigation of logcumulants indicates a larger deviation from Gaussian statistics in the TerraSAR-X data compared with Radarsat-2 measurements. Log-cumulant diagrams are also shown to be a useful tool for discrimination between oil spills and a simulated biogenic slick in both sensors. Multipolarization features show enhanced slick-sea contrasts and a better discrimination between mineral oil spills and other low-backscatter features in Radarsat-2 compared with TerraSAR-X. The presence of a non-Bragg scattering component in the data is revealed for both sensors. The relative contribution of non-Bragg scattering to the total backscatter is found to be higher in the TerraSAR-X data than in the Radarsat-2 data. In general, the non-Bragg component is found to account for a larger part of the backscatter in slick-covered areas compared with clean sea.

Camilla Brekke

Papers

Oil spill detection by satellite remote sensing

Oil Spill Detection in Radarsat and Envisat SAR Images

Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features

Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations

Comparing Near-Coincident C- and X-Band SAR Acquisitions of Marine Oil Spills