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Data Registration
Published in S. Sitharama Iyengar, Richard R. Brooks, Distributed Sensor Networks, 2016
Richard R. Brooks, Jacob Lamb, Lynne L. Grewe, Juan Deng
An example of a system that performs registration over only portions of the images is given in [Blum 04] which uses IR, millimeter wave and visual images for concealed weapon detection. First, either the IR or millimeter wave image is used to detect areas that contain potential weapons. It is only these regions that are registered and merged. A multi-resolution image mosaic technique is applied for the registration and merging of these areas to the visual image. The multi-resolution image mosaic algorithm involves applying weighted averaging operations in the predefined area at different resolutions. Figure 20.28 shows the operations involved in this system and Figure 20.29 the results.
An improved coverage-oriented retrieval algorithm for large-area remote sensing data
Published in International Journal of Digital Earth, 2022
Xuejing Yan, Shibin Liu, Wei Liu, Qin Dai
An improved coverage-oriented retrieval algorithm for large-area remote sensing data is proposed in this paper to address the issue of efficiency. Three approaches based on Arcpy, PyQGIS, and GeoPandas were adopted. The proposed algorithm can efficiently and automatically select target-area data to ensure that the retrieval results are nearest the target time using the fewest number of images. Subsequent image mosaic task is reduced, and services for various remote sensing applications can be better provided. When images are retrieved for a large-scale area, manual selection is likely to cause omission of data or multiple selections. The proposed algorithm uses an iterative loop in which each step selects the image with the nearest imaging time and the largest contribution to the uncovered target area. The cycle ends when the target area is completely covered, avoiding the problem of missed and redundant selections. In this study, the effectiveness of the algorithm is verified through experiments, which can replace manual data selection and greatly improve the retrieval efficiency and accuracy. By comparing three approaches based on Arcpy, PyQGIS, and GeoPandas, it is concluded that the approach based on GeoPandas is most suitable for data retrieval of large-scale areas.