Abstract
High-quality and large-scale image composites are increasingly important for a variety of applications. Yet a number of challenges still exist in the generation of composites with certain desirable qualities such as maintaining the spectral relationship between bands, reduced spatial noise, and consistency across scene boundaries so that large mosaics can be generated. We present a new method for generating pixel-based composite mosaics that achieves these goals. The method, based on a high-dimensional statistic called the `geometric median,' effectively trades a temporal stack of poor quality observations for a single high-quality pixel composite with reduced spatial noise. The method requires no parameters or expert-defined rules. We quantitatively assess its strengths by benchmarking it against two other pixel-based compositing approaches over Tasmania, which is one of the most challenging locations in Australia for obtaining cloud-free imagery.
Highlights
L ARGE-scale image composites are increasingly important for a variety of applications such as land cover mapping [1]–[3], change detection [4]–[6], and the generation of high-quality data to parametrize and validate bio-physical and geo-physical models [7]–[9]
The resulting mosaic is visually appealing as it preserves the spectral relationships among bands resulting in good color balance and does not show any trace of the Landsat footprints
The method achieves a robust representation of the time period and performs well when there are a low number of clear observations. It generally preserves the spectral relationships among bands and produces outputs that contain less spatial noise than other approaches such as the medoid, and one-dimensional median applied to each band separately
Summary
L ARGE-scale image composites are increasingly important for a variety of applications such as land cover mapping [1]–[3], change detection [4]–[6], and the generation of high-quality data to parametrize and validate bio-physical and geo-physical models [7]–[9]. Since first introduced by Holben [10] as a method to reduce cloud and aerosol contamination in advanced very high resolution radiometer time series, a number of compositing methodologies have been proposed (see [1], [11]–[13]). Challenges such as maintaining the spectral relationship between bands, mitigating against boundary artifacts due to mosaicking scenes from different epochs, and ensuring spatial regularity across the mosaic image still exist. The greater availability of satellite imagery has resulted in demand to provide
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