A review of parallel computing for large-scale remote sensing image mosaicking

Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation process. The state of the art of this field has not yet been summarized, which is, however, essential for a better understanding and for further research of image mosaicking parallelism on a large scale. This paper provides a perspective on the current state of image mosaicking parallelization for large scale applications. We firstly introduce the motivation of image mosaicking parallel for large scale application, and analyze the difficulty and problem of parallel image mosaicking at large scale such as scheduling with huge number of dependent tasks, programming with multiple-step procedure, dealing with frequent I/O operation. Then we summarize the existing studies of parallel computing in image mosaicking for large scale applications with respect to problem decomposition and parallel strategy, parallel architecture, task schedule strategy and implementation of image mosaicking parallelization. Finally, the key problems and future potential research directions for image mosaicking are addressed

The Lick Observatory Supernova Search

We report here the current status of the Lick Observatory Supernova Search (LOSS) with the Katman Automatic Imaging Telescope (KAIT). The progress on both the hardware and the software of the system is described, and we present a list of recent discoveries. LOSS is the world' most successful search engine for nearby supernovae.Comment: 4 pages, 1 figure, Submitted to the proceedings of the 10th Annual October Astrophysics Conference in Maryland on Cosmic Explosion

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

The QUEST large area CCD camera

We have designed, constructed, and put into operation a very large area CCD camera that covers the field of view of the 1.2 m Samuel Oschin Schmidt Telescope at the Palomar Observatory. The camera consists of 112 CCDs arranged in a mosaic of four rows with 28 CCDs each. The CCDs are 600 x 2400 pixel Sarnoff thinned, back-illuminated devices with 13 µm x 13 µm pixels. The camera covers an area of 4.6° x 3.6° on the sky with an active area of 9.6 deg_2. This camera has been installed at the prime focus of the telescope and commissioned, and scientific-quality observations on the Palomar-QUEST Variability Sky Survey were started in 2003 September. The design considerations, construction features, and performance parameters of this camera are described in this paper