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Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets. [ABSTRACT FROM AUTHOR]

Target detection in nonhomogeneous sea clutter environments is a complex and challenging task due to the capture effect from interfering outliers and the clutter edge effect from background intensity transitions. For synthetic aperture radar (SAR) measurements, those issues are commonly caused by multiple targets and meteorological and oceanographic phenomena, respectively. This paper proposes a segmentation-based constant false-alarm rate (CFAR) detection algorithm using truncated statistics (TS) for multilooked intensity (MLI) SAR imagery, which simultaneously addresses both issues. From our previous work, TS is a useful tool when the region of interest (ROI) is contaminated by multiple nonclutter pixels. Within each ROI confined by the reference window, the proposed scheme implements an automatic image segmentation algorithm, which performs a finite mixture model estimation with a modified expectation–maximization algorithm. Data truncation is applied here to exclude all possible statistically interfering classes, and sample modeling is based upon the truncated two-parameter gamma model. Next, CFAR detection is conducted pixel by pixel, utilizing the statistical information obtained from the segmentation process within the local reference window. The segmentation-based CFAR detection scheme is examined with real Radarsat-2 MLI SAR imagery. Compared with the conventional CFAR detection approaches, our proposal provides improved background clutter modeling and robust detection performance in nonhomogeneous clutter environments. [ABSTRACT FROM AUTHOR]

Recent airborne field campaigns making passive microwave measurements have observed some radio frequency interference (RFI) that remained undetected by the kurtosis RFI-detection algorithm. The current pulsed-sinusoidal model for RFI does not explain this anomalous behavior of the detection algorithm. In this paper, a new RFI model is developed that takes into account multiple RFI sources within an antenna footprint. The performance of the kurtosis algorithm with the new model is evaluated. The behavior of the kurtosis detection algorithm under central-limit conditions due to multiple sources is experimentally verified. The new RFI model offers a plausible explanation for the lack of detection by the kurtosis algorithm of the RFI otherwise observed. [ABSTRACT FROM AUTHOR]

The presence of radio frequency interferences (RFIs) in the data collected by the Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency is larger than what was anticipated, and it has a strong impact on the retrieval of these geophysical parameters. The first efforts to deal with this problem were proposed by DEIMOS Engenharia in the Level 1 Prototype Processor released in November 2010, and they are also a part of its latest versions. DEIMOS focused on three key aspects to detect the RFIs, to mitigate these sources, and to flag the data in SMOS products affected by RFIs. The detection algorithm is based on a statistic approach that computes the most probable position of the RFI source based on multiangular observations made by SMOS. The mitigation algorithm has been designed taking as reference the existing technique to remove the sun effect. The mechanism to flag the data has been developed in cooperation with the European Space Agency and consists in flagging geolocated pixels according to the strength of the RFI. In this paper, three algorithms will be presented, as well as their results. For completion, a high-level description of the data processing for SMOS is also a part of this text. [ABSTRACT FROM AUTHOR]

The automatic seismic signal detection constitutes a very interesting and challenging task. The main difficulty in solving this problem is attributed to the fact that both the statistical properties of seismic noise and the characteristics of the recorded events are in general unknown. In this paper, by exploiting the particular nature of the signals we are treating, as well as a number of very interesting properties possessed by exchangeable random variables, we formulate the problem at hand as a record segmentation one and propose the use of two functionally linked test statistics for its efficient and robust solution, in a two-step procedure. By following this approach, we succeed not only in detecting a seismic wave arrival, but also in identifying the entire interval occupied by the signal, while minimizing the number of the required parameters. The performance of the proposed technique is confirmed by a series of experiments, both in synthetic and real seismic data sets. [ABSTRACT FROM PUBLISHER]