The pixel-offset method has been utilized as a powerful tool to measure large ground movements. However, L-band spaceborne synthetic aperture radar (SAR) data are often affected by the ionosphere, which produces serious noises in the azimuth component of the pixel offset field, called as azimuth streaks. Here, we propose a new method to mitigate azimuth streaks based on physical modeling. Azimuth streaks cannot be removed by simply combining the known relationship between ionospheric azimuth offset and the ionospheric phase delay with the phase delay obtained by the split-spectrum method. Thus, taking into account that image matching (coregistration) affects the measurement of azimuth offsets, we formulate a theoretical correction formula of azimuth streaks by subtracting the coregistration-induced effects approximated by a polynomial function from ionospheric azimuth offsets modeled using the split-spectrum method. Applying the method to two pairs of Advanced Land Observing Satellite-2 (ALOS-2)/Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) stripmap images which are severely affected by the ionosphere, we demonstrate effective mitigation of azimuth streaks. In the application to the case that no significant ground movement is detected by Global Navigation Satellite System (GNSS), while the standard deviation of azimuth pixel offset before the correction is 87.2 cm, the value after the correction is 29.2 cm, which is comparable to the theoretical measurement accuracy of the azimuth pixel offset. In the application to the 2016 Kumamoto earthquake, we substantially reduce the azimuth streaks and successfully extract the ground movements from the azimuth offset fields within an accuracy of about 20 cm. The result suggests the proposed method enables more accurate and operational estimation of the 3-D ground displacement.

Asynchronous Boolean totalistic cellular automata have recently attracted attention as promising models for implementation by reaction-diffusion systems. It is unknown, however, to what extent they are able to conduct computation. In this paper, we introduce the so-called non-camouflage property, which means that a cell's update is insensitive to neighboring states that equal its own state. This property subsumes the Boolean totalistic property, which signifies the existence of states in a cell's neighborhood, but is not concerned with how many cells are in those states. We argue that the non-camouflage property is extremely useful for the implementation of reaction-diffusion systems, and we construct an asynchronous cellular automaton with this property that is Turing-complete by directly simulating Turing machines. We also construct another asynchronous cellular automaton, but this model incorporates the so-called freezing property [1], which restricts the direction of transitions of each cell to one-way. We show that this model is Turing-complete, since it can simulate the temporal evolution of elementary cellular automata. These results indicate the feasibility of computation by reaction-diffusion systems.