Interferometric synthetic aperture radar (InSAR) is a key tool for the analysis of displacement and stress changes caused by large crustal earthquakes, particularly in remote areas. A challenge for traditional InSAR has been its limited spatial and temporal coverage especially for very large events, whose dimensions exceed the typical swath width of 70–100 km. This problem is addressed by the ALOS‐2 satellite, whose PALSAR‐2 instrument operates in ScanSAR mode, enabling a repeat time of 2 weeks and a swath width of 350 km. Here we present InSAR line‐of‐sight displacement data from ALOS‐2/PALSAR‐2 observations covering the M_w 7.8 Gorkha, Nepal earthquake and its M_w 7.3 aftershock that were acquired within 1 week of each event. The data are made freely available and we encourage their use in models of the fault slip and associated stress changes. The M_w 7.3 aftershock not only extended the rupture area of the main shock toward the east but also left a 20 km gap where the fault has little or no coseismic slip. We estimate this unslipped fault patch has the potential to generate a M_w 6.9 event.

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

In this paper, the authors propose a novel radio frequency interference (RFI) detection and removal method for a synthetic aperture radar (SAR). The proposed method is exclusively sensitive to a pulse-like signal that is temporally transmitted in wide bandwidth. The bandwidth of the RFI is supposed to be similar or wider than the bandwidth of the SAR, while traditional RFI detectors assume that it is narrower and thus it can be removed in the range-frequency and azimuth-time domain using a bandpass filter. On the other hand, the proposed method employs a local autocorrelation-based detection scheme in the range-time and azimuth-frequency domain. The proposed method assumes the existence of another type of RFI, namely, intermittently transmitted wideband (ITWB) RFI. Once ITWB RFI superimposes on the SAR image, its short-pulse duration/wide bandwidth hinders the detection in the range-frequency and azimuth-time domain. Conversely, in the range-time and azimuth-frequency domain, ITWB RFI can be detected easily because its Doppler frequency profile does not fit with backscattered signals from the ground. In this paper, a basic methodology and the experimental results are presented.

This paper proposes a complex-valued convolutional neural network for land form classification and discovery in interferometric synthetic aperture radar (InSAR). Since the amount of satellite-borne SAR data has been increasing drastically, it is necessary to structurize the local features contained in observation data prior to utilization in the so-called big data framework for higher usability. Convolutional neural networks have such potential in general. However, there exists no network that can deal with complex amplitude data obtained in InSAR consistently. In this paper, we propose a complex-valued convolutional neural network to deal with InSAR. We demonstrate that the network classifies slopes and plains adaptively and, moreover, indicates small volcanos similar to a sample volcano (Omuroyama) included in the InSAR data. We also find their characteristic features emerging in the kernels in the convolution layers. These results reveal that the proposed complex-valued convolutional neural network is capable of successfully discovering unidentified lands similar to a prepared sample, which is highly useful for the InSAR data structurization.

In this paper, evaluation results are presented for multi-temporal interferometric coherence analysis using a Synthetic Aperture Radar (SAR) for damage assessment in an urban area. The latest space-borne SARs potentially have a high enough spatial resolution to assess individual buildings. However, interferometric coherence analysis has not been evaluated for its limitation in sensitivity and size of damaged buildings. In particular, the correlation between the coherence analysis and the damage level referred to by architectural assessments has been an open question. In this paper, analytical results using ALOS-2 PALSAR-2 datasets are presented from the 2016 Kumamoto earthquakes in Japan. For reference, building damage was assessed throughout the central urban area and specifically at a catastrophically damaged district. The results show that the buildings should be larger than a window size of the coherence for damage detection, and the damage level should be larger than Level-2 of 5, classified with the European Macroseismic Scale 1998 (EMS-98).

We propose a direction of arrival (DoA) estimation method using a complex-valued neural network (CVNN) for ultrawideband (UWB) systems. We combine a complex-valued spatiotemporal neural network with power-inversion adaptive-array scheme for null-steering DoA estimation. Simulation and experiments demonstrate that the proposed method shows an estimation accuracy higher than that of conventional multiple signal classification method and a spectrum floor lower than that of real-valued neural network. These results suggest that the CVNN deals with signals more properly as wave information in the null synthesis in UWB systems.