Performance of correlation approaches for the evaluation of spatial distortion reductions

Abstract

The analysis of optical remote sensing images often requires a perfect pixel alignment between single bands. Even smallest deviations may degrade the accuracy of subsequent parameter retrieval or lead to the detection of non-existing structures caused by artificial gradients. Hence, a careful pre-processing is essential for minimising spatial non-uniformities such as erroneous co-registration. The results need to be validated and assigned with a quality flag that is unfortunately still not a common practice. In this letter, four broadly used global correlation approaches, namely the two-dimensional Gaussian peak fit, e.g. from Nobach and Honkanen (2005), the poly phase technique from Foroosh et al. (2002), the iterative phase approach from Averbuch and Keller (2002) and its proposed enhancement, were tested for their capacity to serve either as evaluation tool for preceding spatial distortion reductions, e.g. by co-registration, or as global minimiser for generic reduction approaches. For this 8 broadly used test images, 3 Landsat 7 and 3 Landsat 8 samples were artificially sub pixel shifted and degraded by different noise levels resulting in more than 200,000 noise and shift scenarios. Additionally, one state-of-the-art approach was enhanced by 50 % on average for all scenarios and by 280 % on average for all non-degraded images. This study indicates that three out of four approaches can serve as evaluation tools for spatial distortion reductions or as global minimiser even for highly degraded images, whereas proposed enhancement offers highest accuracy and the approach from Foroosh offers best overall performance with regard to considered criteria.

AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data

“…Compared with other intensity-based algorithms such as classical cross-correlation, phase correlation delivers significantly higher accuracy due to a distinct peak in the cross-power spectrum indicating the point of registration [9,[19][20][21]. It delivers excellent co-registration results, even in the case of poor signal-to-noise ratios and substantial ground cover changes between different images, e.g., due to seasonal vegetation dynamics [19,22]. Adding to its robustness against albedo differences and its computational efficiency [23,24], an intensity-based co-registration approach, such as phase correlation, is highly suited for a generic application to multi-sensor remote sensing datasets-provided that the geometric displacements follow a more or less affine or polynomial pattern, which would limit a phase correlation approach [7,9,20].…”

“…This is also supported by [9,22], and potential effects are quantified by implementing a total of five complementary validation techniques. Their individual performance depends on the image content of the input images and the pattern of misregistration.…”

“…This is essential to keep the results of neighbouring displacement vectors comparable, since the precision of the calculated displacement changes with the size of the matching window [11,22]. However, at the edge of the overlap area, it is needed to ensure that the matching window does not protrude into the no-data area of reference or target image.…”

“…The two resulting images in the frequency domain are phase-correlated to generate their cross-power spectrum, which is then transformed back into spatial domain using inverse FFTW [9,13,22,33,34]. The normalized form of the cross-power spectrum in the spatial domain demonstrates a distinct sharp peak at the point of registration of the input images (Figure 2), which can be used for quantification of image displacements [7,9,16,19,24,33].…”

DRILL CORE MINERAL ANALYSIS BY MEANS OF THE HYPERSPECTRAL IMAGING SPECTROMETER HySpex, XRD AND ASD IN PROXIMITY OF THE MÝTINA MAAR, CZECH REPUBLIC

“…In the first step the HySpex raw data is radiometrically transformed into radiance. The VNIR and the SWIR images were then co-registered using an iterative log-polar phase correlation approach Rogass et al (2013). In the second step the reflection standards (Spectralon® panels) were automatically detected in the images.…”