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Journal of Applied Remote Sensing

Multiple temporal mosaicing for Landsat satellite images
Author(s): Yi Guo; Feng Li; Peter A. Caccetta; Drew Devereux
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Paper Abstract

Cloud removal is a very important preprocessing step when using aerial and spaceborne optical sensors for land surface and cover applications. Methods that have been proposed for identifying cloud-affected pixels range from classification and segmentation type approaches applied to individual images to outlier detection type methods applied to time-series of images. The choice of method is influenced by considerations including the requirements of the application, the image characteristics, and how frequently images over a given area are acquired. When many images are acquired in a period where land surface cover exhibits negligible change, an image formed by compositing from a series of images taken in a relatively short period of time will suffice for further analysis. It is highly desirable to fully automate this compositing process. To this end, we propose the multiple temporal mosaicing (MTM) algorithm. It uses, in the first instance, a cloud score for each pixel in the images to separate/partially separate cloud-affected pixels from noncloud pixels. These cloud scores are then combined with the output from existing cloud identification methods and date preference to determine the likelihood of given pixels being considered as good candidates to be included in the final image. Moreover, the spatial smoothness is incorporated to ensure that the pixels of a small neighborhood are from the same image so that the final image looks smoother. We apply MTM to two Landsat scenes. The resulting images show the effectiveness of this method. The methodology can be applied to images acquired from other sensors.

Paper Details

Date Published: 11 March 2017
PDF: 14 pages
J. Appl. Rem. Sens. 11(1) 015021 doi: 10.1117/1.JRS.11.015021
Published in: Journal of Applied Remote Sensing Volume 11, Issue 1
Show Author Affiliations
Yi Guo, Western Sydney Univ. (Australia)
Feng Li, China Academy of Space Technology (China)
Peter A. Caccetta, Commonwealth Scientific and Industrial Research Organisation (Australia)
Drew Devereux, Commonwealth Scientific and Industrial Research Organisation (Australia)

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