Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening

Abstract

Pansharpening algorithms fuse higher spatial resolution panchromatic with lower spatial resolution multispectral imagery to create higher spatial resolution multispectral images. The free-availability and systematic global acquisition of Landsat 8 data indicate an expected need for global coverage and so computationally efficient Landsat 8 pansharpening. This study adapts and evaluates the established, and relatively computationally inexpensive, Brovey and context adaptive Gram Schmidt component substitution (CS) pansharpening methods for application to the Landsat 8 15 m panchromatic and 30 m red, green, blue, and near-infrared bands. The intensity images used by these CS pansharpening methods are derived as a weighted linear combination of the multispectral bands in three different ways using band spectral weights set (i) equally as the reciprocal of the number of bands; (ii) using fixed Landsat 8 spectral response function based (SRFB) weights derived considering laboratory spectra; and (iii) using image specific spectral weights derived by regression between the multispectral and the degraded panchromatic bands. The spatial and spectral distortion and computational cost of the different methods are assessed using Landsat 8 test images acquired over agricultural scenes in South Dakota, China, and India. The results of this study indicate that, for global Landsat 8 application, the context adaptive Gram Schmidt pansharpening with an intensity image defined using the SRFB spectral weights is appropriate. The context adaptive Gram Schmidt pansharpened results had lower distortion than the Brovey results and the least distortion was found using intensity images derived using the SRFB and image specific spectral weights but the computational cost using the image specific weights was greater than the using the SRFB weights. Recommendations for large area Landsat 8 pansharpening application are described briefly and the SRFB spectral weights are provided so users may implement computationally inexpensive Landsat 8 pansharpening themselves.

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

“…In this study the Q2 n values were calculated with respect to non-overlapping adjacent n × n pixel windows and then averaged to yield a single image value. The same n = 32 pixel window dimensions used to assess downscaling Landsat-8 30 m to 15 m [46] was implemented. Figure 3 illustrates the results of Landsat-8 downscaling from 30 m to 20 m using three conventional resampling approaches.…”

“…Unlike many commercial sensors, the 15-m band is not perfectly nested within the lower resolution 30-m bands [46], rather, the panchromatic pixel grid is shifted by 7.5 m in the row and column directions relative to the 30-m pixel grid (Figure 1). …”

“…Unlike many commercial sensors, the 15-m band is not perfectly nested within the lower resolution 30-m bands [46], rather, the panchromatic pixel grid is shifted by 7.5 m in the row and column directions relative to the 30-m pixel grid (Figure 1). The 30-m bands are resampled to the 15-m panchromatic grid using a method to compensate for this kind of pixel shift [47].…”

“…In this study an implementation of the CS Brovey method [45] that was designed for Landsat-8 data and is computationally efficient and so appropriate for global application is implemented [46]. The method includes three steps: (1) (1) by modulating and adding the difference (the spatial details) between the derived intensity image in (2) and the 15-m panchromatic image.…”

“…The spectral weights in Equation (1) were derived considering a large number of laboratory spectra and the Landsat-8 OLI spectral response functions [46]. The intensity image is derived using the red, green, and blue bands as they overlap spectrally with the panchromatic band ( Table 1).…”