Abstract
A band-to-band mis-registration (BBMR) error often occurs in remote sensing (RS) images acquired by multi-spectral push broom spectrometers such as the Sentinel-2 Multi-spectral Instrument (MSI), leading to adverse impacts on the reliability of further RS applications. Although the systematic band-to-band registration conducted during the image production process corrects most BBMR errors, there are still quite a few images being observed with discernible BBMR. Thus, a quick BBMR detection method is needed to assess the quality of online RS products. We here propose a hybrid framework for detecting BBMR between the visible bands in MSI images. This framework comprises three main steps: first, candidate chips are captured based on Google Earth Engine (GEE) spatial analysis functions to shrink the valid areas inside image scenes as potential target chips. The redundant data pertaining to the local operation process are thus narrowed down. Second, spectral abnormal areas are precisely extracted from inside every single chip, excluding the influences of clouds and water surfaces. Finally, the abnormal areas are matched pixel by pixel between bands, and the best-fit coordinates are then determined to compare with tolerance. Here, the proposed method was applied to 71,493 scenes of MSI Level-1C images covering China and its surrounding areas on the GEE platform. From these images, 4356 chips from 442 scenes were detected with inter-band offsets among the visible bands. Further manual visual inspection revealed that the proposed method had an accuracy of 98.07% at the chip scale and 88.46% at the scene scale.
Highlights
The band-to-band mis-registration (BBMR) of multi-spectral remote sensing (RS) images refers to the misalignment between bands caused by differences in the imaging time or angle between detection elements [1]
Comparing Multi-spectral Instrument (MSI) images with and without BBMR over the same area, we found that the relationships among the visible bands differed significantly (Figure 1a–d)
The proposed method was applied to 71,493 scenes of MSI Level-1C top of atmosphere (TOA) reflectance images covering China and its surrounding areas (58◦ 430 3700 N–0◦ 230 1500 S, 142◦ 290 4000 E–67◦ 80 4400 E) acquired from 21 July 2015 to 28 February 2016, the first few months after the launch of Sentinel-2A
Summary
The band-to-band mis-registration (BBMR) of multi-spectral remote sensing (RS) images refers to the misalignment between bands caused by differences in the imaging time or angle between detection elements [1]. For multi-spectral push broom spectrometers without a beam splitter, e.g., the Sentinel-2 Multi-spectral Instrument (MSI), different spectral band stripe filters are mounted on every single detector. This imposes a slight time offset between each spectral channel sensor during imaging [5], where long-track displacement is approximately 14 km for MSI images [6]. After systematic band-to-band registration (BBR), the inter-band geometric displacement of MSI images should be under
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