A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images

Abstract

Compositing is a fundamental pre-processing for remote sensing images. Landsat series optical satellite images are influenced by cloud coverage, acquisition time, sensor types, and seasons, which make it difficult to obtain continuous cloud-free observations. It limits the potential use and analysis of time series images. Therefore, global change researchers urgently need to ‘composite’ multi-sensor and multi-temporal images. Many previous studies have used isolated pixel-based algorithms to composite Landsat images; however, this study is different and develops a batch pixel-based algorithm for composing continuous cloud-free Landsat images. The algorithm chooses the best scene as the reference image using the user-specified image ID or related parameters. Further, it accepts all valid pixels in the reference image as the main part of the result and develops a priority coefficient model. Development of this model is based on the criteria of five factors including cloud coverage, acquisition time, acquisition year, observation seasons, and sensor types to select substitutions for the missing pixels in batches and to merge them into the final composition. This proposed batch pixel-based algorithm may provide reasonable compositing results on the basis of the experimental test results of all Landsat 8 images in 2019 and the visualization results of 12 locations in 2020. In comparison with the isolated pixel-based algorithms, our algorithm eliminates band dispersion, requires fewer images, and enhances the composition’s pixel concentration considerably. The algorithm provides a complete and practical framework for time series image processing for Landsat series satellites, and has the potential to be applied to other optical satellite images as well.