11 March 2017 Multiple temporal mosaicing for Landsat satellite images
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Yi Guo, Yi Guo, Feng Li, Feng Li, Peter A. Caccetta, Peter A. Caccetta, Drew Devereux, Drew Devereux, } "Multiple temporal mosaicing for Landsat satellite images," Journal of Applied Remote Sensing 11(1), 015021 (11 March 2017). https://doi.org/10.1117/1.JRS.11.015021 . Submission: Received: 23 June 2016; Accepted: 21 February 2017
Received: 23 June 2016; Accepted: 21 February 2017; Published: 11 March 2017
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