Advanced Land Observing Satellite-4 (ALOS-4) is a spaceborne high-resolution and wide-swath synthetic aperture radar (HRWS-SAR) that uses a variable pulse repetition interval (VPRI) technique to achieve continuous wide imaging. In some ALOS-4 images, azimuth fractional ambiguity caused by the VPRI is observed, and it differs from the usual integer ambiguity resulting from inter-channel errors in that it occurs at smaller intervals. In this paper, we propose a sequential Doppler offset (SDO) method for locating the original target (OT) that causes azimuth fractional ambiguity. First, the ratio of the interval of integer ambiguity to that of fractional ambiguity is obtained, which is used to generate SAR images with different Doppler center frequencies. Second, the coherence between the sum image of the generated images and the image with a zero Doppler center frequency is calculated. Third, some points with coherence greater than a threshold are selected based on the coherence. Finally, the final OT is obtained by detecting the filtered selected points. Some experiments are conducted based on ALOS-4 L1.2 data, and the results demonstrate that the method locates the OT accurately. In short, the proposed method provides a starting point for fractional ambiguity suppression in HRWS-SAR.

Multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) has potential for high-resolution and wide-swath (HRWS) imaging. Current research mainly focuses on its echo separation. However, how to achieve HRWS imaging based on separated echoes has been rarely discussed and remains as if all echoes are separated and reconstructed ideally so that some conventional imaging algorithms can properly focus. Thus, this paper proposes an HRWS imaging mode for spaceborne MIMO-SAR, named virtual delay-emission (VDE). First, the VDE delays the transmission time of some channels to reduce the effective phase center (EPC) redundancy of MIMO-SAR and increase the effective antenna area. Second, the VDE transmits orthogonal waveforms with the same autocorrelation function. Third, the VDE separates the mixed echoes and reconstructs the separated echoes in the frequency domain to achieve low azimuth ambiguity and high-resolution imaging. Some system simulations based on ALOS-2/-4-like system parameters are conducted, and the results demonstrate that the VDE achieves the 3m/900kmmode if the system hardware is implemented properly.

In microwave remote sensing, radio frequency interference (RFI) caused by other microwave systems has been a critical issue. RFI degrades the quality of synthetic aperture radar (SAR) images and disturbs phase and polarimetric information. Therefore, RFI must be detected and mitigated correctly to analyze SAR data accurately. In this paper, we propose a novel RFI detection method based on the polarization states of received signals in either the time or frequency domain. Our study demonstrates that RFI from artificial sources is often more polarized than radar echoes from natural scatterers. We use the degree of polarization (DoP) to measure the polarization states and detect highly-polarized RFI. The simulation results indicate that the proposed method was more accurate than the conventional power-spectrum-based methods and improved the detection limit by 4 dB compared to the conventional method based on the similarity between polarizations. We demonstrate the effectiveness of the proposed method by using data obtained by Phased Array type L-band Synthetic Aperture Radar (PAL-SAR) aboard Advanced Land Observing Satellite (ALOS).

This work introduces the ongoing initiatives by the "RFI in Remote Sensing Working Group" within the IEEE Standards Association. The Working Group is taking the lead in standardizing the evaluation of radio frequency interference (RFI) impact on spaceborne microwave remote sensing. This standardization effort aims to enhance the monitoring of RFI and improve the effectiveness of sharing information related to it. It is developing a standard titled "P4006 Standard for Remote Sensing Frequency Band Radio Frequency Interference (RFI) Impact Assessment". This paper presents these efforts and highlights the recent activities undertaken by this working group.

In repeat-pass interferometric synthetic aperture radar (In-SAR), the coherence loss derived from temporal decorrelation can be modeled using an exponential decay function. Many researches have investigated the relationships between decay parameters over various land-cover types for C-band SAR satellites. Although ALOS PALSAR provides many L-band SAR images, its revisit time was insufficient for understanding the rapid exponential drop in temporal decorrelation. Due to the lack of an appropriate archived dataset, analyzing the decay parameters for L-band SAR was a challenging task. In this study, we processed four-year interferometric pairs derived from ALOS-2 PALSAR-2 full-aperture ScanSAR images. This dataset has a 14 day minimum temporal interval. We selected Nairobi, Kenya, in the middle of the East African rift, as the study area and investigated how decay functions differ over multiple land-cover types. In addition, we compared decay models for Sentinel-1 interferograms. This study demonstrates that the decay parameters of L-band coherence completely differs across forest types, whereas C-band coherence is low coherence regardless of forest type.

In microwave remote sensing, radio frequency interference (RFI), which is caused by other systems such as radars, telecommunications, and navigation systems, has been a serious problem. RFI generates haze-like or linear-pattern artifacts in synthetic aperture radar (SAR) images and degrades their quality. Therefore, it is mandatory to detect and mitigate RFI properly. In this paper, we propose an RFI-detection method using degree of polarization (DoP) which focuses on the difference of polarization states between RFI and useful signals.

In recent years, the applications of complex-valued neural networks (CVNNs) have gained prominence in radar-related domains, notably in synthetic aperture radar and ground-penetrating radar. Unlike traditional real-valued neural networks, CVNNs encompass both amplitude and phase components, for inputs, weights, bias, and activations. In this paper, we adopt the inverse mapping architecture for neural network explainability to conduct feature importance analysis for enhancing the interpretability and transparency of CVNN’s decision. We propose this method for first identifying built-up areas, and then determining the prominent input features by inverse mapping. The feedforward CVNN achieved high training and validation accuracies of 97.09% and 99.25%, respectively. Additionally, the inverse mapping results identify the most significant contributing input features.

The Goldstein filter is one of the most commonly used methods to reduce noise in interferometric synthetic aperture radar data. However, the low filtering performance in highly noisy areas remains unsolved. In this paper, we propose an adaptive filter in the biquaternion Fourier domain (BQFD filter) to improve the filtering performance even in highly noisy regions. This method represents PolInSAR data in a biquaternion form to process the interferometric fringes of different polarimetric channels as a single signal. By using the biquaternion Fourier transform, the fringes can be effectively separated from the noise because of the high correlation of the fringes and the low correlation of the noise between the polarimetric channels. Experimental results show that digital elevation models generated with the BQFD filter are more accurate than those generated with the Goldstein filter.

Ground penetrating radar (GPR) based landmine detection has advantages such as high safety and high efficiency. There are various methods to process data acquired from GPR systems. A common method is the Riemannian quaternion self-organizing map (RQSOM), which effectively enables self-organization of polarization data in quaternion form for visualization. However, RQSOM does not take into account the phase information of scattering components. Studies have shown that phase information is correlated with polarization information and can be effectively integrated into a new form, phasor quaternion (PQ). To visualize PQ-type feature vectors, in this paper, we propose a novel algrithm, Riemannian phasor quaternion self-organizing map (RPQSOM). RPQSOM utilizes the geometric characteristics of the phasor part to effectively learn useful information in PQ, thereby achieving superior visualization performance. We conduct experiments for visualizing a mock landmine. The experimental results demonstrate that RPQSOM fulfills our design objectives and gets better visualization results compared to RQSOM.

Interferometric SAR time series analysis using multilooked interferograms has measured ground deformation with high accuracy over large areas, but is known to be contaminated by a systematic bias when using only short-term interferograms. This bias, also referred to as a "fading signal", has been investigated using the abundant time series data from Sentinel-1 C-band SAR, and soil moisture and biomass changes have been suggested as two possible causes for the observed bias. Several correction methods have been proposed to mitigate the phase bias, assuming a periodic observation strategy. The phase bias is supposed to increase with the wavelength, but the bias in ALOS-2 L-band SAR has not been investigated in detail because the data distribution policy restricts its users. In addition, these correction methods are not suitable for the infrequently observed data such as ALOS-2. In this paper, we investigated the systematic bias in ALOS-2 multilooked interferograms with respect to various interferometric pairs. We also corrected the phase bias using the noise-filtering technique. The bias in ALOS-2 multilooked interferograms showed >3 mm/year with an average temporal baseline of 144 days, which is equivalent to that of Sentinel-1 with the temporal baseline of 78 days. That is, the bias effect toward the temporal baseline was larger in ALOS-2 than in Sentinel-1. The noise-filtering technique could mitigate the bias with less pairs regardless of land-use.

Waveform diversity is an important technique to extend the observation coverage of synthetic aperture radar (SAR) without sacrificing spatial resolution and power consumption. Applying pseudorandom noise sequence (PN-sequence) to frequency modulation chirp (FM chirp) pulses is one of the possible solutions. In this paper, we propose to optimize the PN-sequence with a genetic algorithm (GA) to improve the range ambiguity suppression in SAR images of multiple swaths compared to the existing M-sequence. The imaging simulations show that the proposed method succeed in separating range ambiguous signals from multiple swaths and suppressing range ambiguity by 24.9 to 25.3 dB compared to the case using FM chirp pulse alone.

Recently, recurrent neural networks (RNNs) and the free energy principle (FEP) have been attracting much attention. FEP includes the so-called predictive coding (PC), in which the RNN input signals are coded as the difference between the RNN prediction output signals and original input signals. This paper proposes a new method for processing time-series data in RNNs, namely, time-series forecasting coding (TFC), developed by being based on the idea of PC. We consider multi-step ahead forecasting task with the following two schemes in our proposed TFC, i.e., smallest-delay feedback TFC and synchronous (temporally matching) feedback TFC. The latter is a natural extension of conventional PC. Experimental results show that the smallest-delay TFC presents the highest performance, while synchronous TFC shows comparable or lower performance, and then the conventional method indicates the lowest. This result suggests that the concept of PC is important even in time-series data processing, and that small delay is more important than synchronicity. This may have significant implications to the conceptual fundamentals of the PC and thus of the FEP.

Suppression of range ambiguity is one of the most important issues in Synthetic Aperture Radar (SAR) observations. Range ambiguity is a phenomenon caused by scattered signals from outside of the desired observation range illuminated by preceding and/or following pulses, resulting in a blurred signal in the focused SAR image. Existing solutions such as waveform diversity-based methods and digital-beamforming (DBF) systems increase the complexity of SAR in both hardware and software domains. On the other hand, recent emergence of commercial SAR services, which apply a constellation of small satellites, demands a simpler solution. To meet this demand, we propose to solve the problem by use of the similarity between the range ambiguous signal and the observation swath signal of another acquisition. That is, we consider the case which two satellites observe the adjacent swaths independently so that an ambiguous swath for one satellite becomes the observation swath of the other one. Experimental results showed that the proposed approach makes it easy to detect and suppress strong range ambiguities.

This letter proposes a novel synthetic aperture radar (SAR) time series analysis method based on the scattering power decomposition algorithm with a reasonable ensemble average in both temporal and spatial domains. We reveal that the ensemble average is effective not only in the spatial domain but also in the temporal–spatial domains in the scattering power decomposition. That is, if we extend the ensemble average window in the temporal domain, the proposed method can accurately achieve volume scattering power with a higher spatial resolution than conventional approaches. The precise volume scattering power serves accurate forest monitoring. As an application, we performed forest disturbance detection in the Amazon rainforest using Sentinel-1 time series data. The proposed method detected the disturbances earlier, in less than 2 months, compared to other methods that take about 3 months.

The Goldstein-Werner (GW) filter is one of the most widely used methods to suppress noise in interferometric synthetic aperture radar (InSAR) data. However, the insufficient filtering performance in highly noisy areas remains unsolved. In this article, we propose a biquaternion Fourier domain (BQFD) filter to improve the filtering performance even in highly noisy areas. This method represents polarimetric synthetic aperture radar interferometry (PolInSAR) data in a biquaternion form to process interferometric fringes of different polarimetric channels as a single signal. By using the biquaternion Fourier transform (BQFT), the fringes can be effectively separated from noise due to the high correlation of the fringes and the low correlation of noise between the polarimetric channels. In the experiments, we find that the BQFD filter produces more accurate digital elevation models (DEMs) than those produced with the GW filter. We also find that the accuracy of DEMs can be further improved by choosing an axis of the BQFT adaptively in each filtering window.

Quaternion neural networks (QNNs) form a class of neural networks constructed with quaternion numbers. They are suitable for processing 3-D features with fewer trainable free parameters than real-valued neural networks (RVNNs). This article proposes symbol detection in wireless polarization-shift-keying (PolSK) communications by employing QNNs. We demonstrate that quaternion plays a crucial role in the symbol detection of PolSK signals. Existing artificial-intelligence communication studies mainly focus on RVNN-based symbol detection in digital modulations having constellations in complex plane. However, in PolSK, information symbols are represented as the state of polarization, which can be mapped on the Poincare sphere and thus its symbols have a 3-D data structure. Quaternion algebra offers a unified representation to process 3-D data with rotational invariance and, therefore, it keeps the internal relationship among three components of a PolSK symbol. Hence, we can expect that QNNs learn the distribution of received symbols on the Poincare sphere with higher consistency to detect the transmitted symbols more efficiently than RVNNs. We compare PolSK symbol detection accuracy of two types of QNNs, RVNN, existing methods such as least-square and minimum-mean-square-error channel estimations, as well as detection knowing perfect channel state information (CSI). Simulation results including symbol error rate show that the proposed QNNs outperform the existing estimation methods and that they reach better results with two to three times fewer free parameters than the RVNN. We find that QNN processing will bring practical use of PolSK communications.

We propose a non-destructive method to estimate dielectric constant distribution based on radar image focusing. Water leaks from pipes and deterioration of materials in buildings have a significant impact on building users. However, they are often covered with materials, and periodic inspection is time-consuming. Electromagnetic radar is capable of detecting material changes since the dielectric constant significantly changes where the material is deteriorated or flooded. Several methods have been proposed to estimate a uniform dielectric constant of materials by using electromagnetic radar. In this paper, we obtain spatially-changing constant distribution by using buried objects such as pipes and rebars in such a manner that their radar image obtain a better focus, which means that the objects are clearly visualized.

We propose a feature synthesis and extraction method by focusing on target phase for weakly scattering objects. This method is capable of distinguishing weakly scattering landmines from clutter. Landmines left in various regions in the world have been a serious problem, and mine detection for their clearance is an important issue. Along with techniques for detecting buried objects, determining whether a detected object is a landmine or not is also crucial for efficient clearance. Various methods for distinction of landmines have been proposed, but it has been a difficult task. In this paper, we propose a method to extract features of weakly scattering buried objects such as plastic landmines by synthesizing complex-valued scattering information and excluding phase rotation due to propagation.

Precise classification of glacier surfaces is crucial for glacial health monitoring. Conventionally, in multispectral optical remote sensing, normalized indices have been used for glacial surface classification. These indices are obtained empirically by observing the difference in spectral reflectance of the target in specific bands. However, as the number of satellites, sensors and observation bands increase, there is a need for a more automated method for determining application-specific normalized indices. In this paper, we propose the use of all the bands of Sentinel-2 optical sensor for generating multiple normalized indices and the determination of application-specific significant indices by using inverse mapping. We use the normalized indices for pixel-by-pixel classification of a glacial region and observe an overall 84.81% accuracy compared to the ground truth data. Then, we apply inverse mapping dynamics to the classification results to discover new indices useful for glacier classification.

In this paper, we propose the attention-based reservoir computing and conduct an experiment of InSAR phase filtering. We show that the proposed method can estimate the wrapped phase values more accurately compared with a conventional method using only reservoir computing networks.

In this paper, we describe the attempt of the Frequency Allocation in Remote Sensing Technical Committee (FARS-TC) from the IEEE Geoscience and Remote Sensing Society (GRSS) to standardize the impact assessment of radio frequency interference (RFI) especially in the field of spaceborne remote sensing. Multiple radio services have been sharing a common radio band with remote sensing satellites, resulting in inevitable interferences between each other. In other cases, some services accidentally emit their signal to nearby frequency band. Detecting interference and locating its source is a necessary task to guarantee data integrity. Given that remote sensing satellites carry unique payloads, the effect that RFI have depends on each sensor. For this reason, the RFI impact assessments have been usually developed with one mission or application in mind.Standardizing the impact assessment of RFI will benefit to make a public identification system so that we can monitor RFI effectively. In this paper, we introduce the working group in the IEEE Standards Association named "P4006 Standard for Remote Sensing Frequency Band Radio Frequency Interference (RFI) Impact Assessment" which is handled by the IEEE GRSS FARS-TC and its recent activities.

In this paper, we propose a novel disaster damage detection method using Synthetic Aperture Radar (SAR) interferometric analysis. SAR interferometric coherence analysis is an effective method for disaster monitoring especially in the urban area, where the amplitude of SAR image changes only slightly unless the damage level is high. One drawback of the interferometric coherence-based analysis is the existence of the Cramér-Rao lower bound. That is, a small spatial window for its ensemble averaging leads significant bias while a large window makes its spatial resolution worse. To solve this problem, we propose to extend the ensemble average window towards temporal domain by increasing the number of interferometric pairs. Conventional methods which use multiple interferometric pairs firstly calculate coherence values independently. Instead, the proposed method unifies the interferograms first. We report some preliminary experimental results showing the effectiveness of the proposed method.

As the resolution of polarimetric synthetic aperture radar (PolSAR) and polarimetric interferometric SAR (PolInSAR) data increases, coherent processing of polarization and phase information becomes more important in wide applications including land surface classification and digital elevation model (DEM) generation. In this paper, we propose biquaternion neural networks (BQNNs) to process polarization and phase information pixel by pixel. We first propose a biquaternion neuron model. Then, we derive the backpropagation of BQNNs using Wirtinger derivatives. Finally, we show that our proposed neuron model can directly transform physical scattering mechanisms into different scattering mechanisms in a coherent manner, which is essential for feature extraction and their effective combinations.

Ground penetrating radar (GPR) based landmine detection has advantages such as high safety and high efficiency. There are various methods to process the data obtained from GPR systems. One of the common methods is Riemannian quaternion self-organizing map (RQSOM), which can effectively make the polarization data self-organize for visualization. However, RQSOM cannot take into account the spatial degree of polarization (DoP) of the extracted data. Spatial DoP contains useful information for landmine visualization. To overcome the limitation, in this paper, we propose a novel algorithm, generalized Riemannian quaternion self-organizing map (GRQSOM), which utilize both polarization and spatial DoP during self-organization. Thus, better visualization performance can be obtained. We conduct experiments for the visualization of a mock landmine. The experimental results show that GRQSOM achieves our design and gets better visualization results compared with RQSOM.

To extend the observation coverage of synthetic aperture radar (SAR), existing methods cost spatial resolution and/or power consumption. In this paper, we propose to separate range ambiguous signals by applying Pseudo Noise sequence (PN-sequence) to Frequency Modulation chirp (FM-chirp) pulses and observe multiple observation swaths. Experimental results show that the proposed method successfully separates multiple range ambiguous signals. Compared with the cases using the traditional FM-chirp pulses, the proposed method suppresses range ambiguities 21.6 to 23.8 dB.

This paper proposes quaternion-neural-network (QNN) based robust symbol detection in wireless polarization-shift-keying (PolSK) communications. In these years, many ma-chine learning approaches based on real-valued neural-networks (RVNNs) outperform conventional methods for symbol detection in wireless fading channels. However, existing studies mainly focuses on digital modulations having constellation diagrams in complex planes, such as phase shift keying and amplitude phase shift keying. In PolSK, information symbols are represented as the state of polarization (SOP) of the propagating wave. Since a SOP can be described by Stokes parameters and mapped on the Poincare sphere, its symbols have a three-dimensional (3-D) data structure. Quaternion-algebra expressions offer a unified representation to process 3-D data with rotational invariance and thus it keeps the internal relationship among three components of a PolSK symbol. Hence, QNNs learn the distribution of received symbols on the Poincare sphere with higher consistency and detect the transmitted symbols more efficiently than RVNNs. We compare PolSK symbol detection accuracy of two types of QNNs, RVNN and two conventional channel estimation methods, namely least square and minimum mean square error. Simulation results such as symbol error rates show that the proposed QNNs outperform other adaptive symbol detection methods robustly. We find that the QNN processing brings practical use of PolSK communications.

Non-line-of-sight (NLOS) optical wireless communication (OWC) based on atmospheric scattering attracts more and more attention due to their mitigation of pointing, acquisition, and tracking requirement. However, the channel relies on scattering radiation and thus causes significant dispersion and inter-symbol interference (ISI), which limits high data rate communications. Reservoir computing (RC) is a computational framework derived from recurrent neural networks (RNNs). It is suitable for sequential data processing with low training cost. In contrast to multilayer perceptron (MLP)-based symbolwise detectors that treat ISI as noise, a RC performs sequence detection that takes ISI into account. In this paper, we propose to employ the RC approach to detect transmitted symbols of NLOS OWC systems without knowing channel state information. Our simulations show that the RC can perform symbol detection in various channel conditions and it brings lower bit error rate results than the MLP-based detection.

Ground penetrating radar (GPR) based landmine detection has advantages such as high safety and high efficiency. There are various methods to process the data obtained from GPR systems. One of the commonly used methods is to visualize the quaternion-type polarization data by quaternion self-organizing map (QSOM). However, QSOM can not take into account the geometric property of the polarization data. Then, degree-of-polarization (DoP) error is introduced in the self-organization process of QSOM, which leads to unsatisfactory visualization results. To overcome the limitation, in this paper, we propose a novel processing, Riemannian quaternion self-organizing map (RQSOM), which takes into account the geometric property to eliminate the DoP error with the help of logarithmic and exponential maps in Riemannian geometry. We analyze its basic dynamics and compare it with that of QSOM. We conduct experiments to visualize a mock plastic landmine with QSOM and RQSOM. The experimental results show that RQSOM realizes expected self-organization dynamics, thereby achieving better visualization results compared to QSOM.

This paper proposes model-based homogeneity (MBH) to extend compressed sensing (CS) for detection of subsurface objects. The scattered waves at landmines have features spe-cific to their material and structure. The features spread over a landmine, showing its shape. We calculate the MBH value from spatial distribution of scattering feature vectors by using a model having the landmine shape. We can utilize CS with MBH because the result of MBH calculation is sparse in general. This method eliminates clutter and reduces mea-surement points. Experiments demonstrate that this method makes the time for detection one-twentieth of that in a conventional method.

With the rise in the number of remote-sensing satellites in the past decade, interest has been drawn towards the fusion of satellite data. This study investigates the fusion of features obtained from the L-band ALOS-2 Synthetic aperture radar (SAR) and the Sentinel-2 optical satellite by the use of neural networks. It identifies the prominent features by using an inverse-mapping algorithm. The fusion results show an increased classification accuracy compared to that of independent sensors. The algorithms have been tested on the data of the 2018 earthquake in Sulawesi, Indonesia.

In this paper, we propose an interferometric synthetic aperture radar (InSAR) phase unwrapping method by use of dynamics of the Kuramoto model that is one of phase oscil-latory networks. The network generates a quality map which induces a proper unwrapping path by solving singular points (SPs) or residues. We show that our proposal unwraps in-terferometric phase with higher accuracy compared to other model-algorithm-based unwrapping methods.

Quaternion convolutional neural networks (QCNNs) are in-herently useful for image processing and PolSAR land classification, since they can learn the relationship between the components of input vectors with quaternionic rotations. However, conventional QCNNs fix their rotation axes represented by quaternion weights, resulting in reduction of the degree of freedom (DoF) and the lose of the expression ability. In this paper, we propose QCNNs which learn all the four parameters of the quaternion weights by backpropagation. They perform learning with the maximum of DoF so that they take full advantages of quaternion learning. In addition, we use two totally different features, namely, Pauli RGB features and normalized Stokes vectors. We experimentally found that Pauli RGB features are suitable for discrimination between town and forest, and Stokes vectors between water and grass. Combining their two results complementarily improves classification results. Our proposed QCNNs show the best classification performance compared with real-valued convolutional neural networks and fixed-axis QCNN. These results demonstrate the strength of the proposed QCNN in adaptive polarization processing in multimodal data in the PolSAR field.

Synthetic Aperture Radar (SAR) has been sharing its radio band with other systems. Similar to recent Dynamic Spec-trum Sharing (DSS) in switching from 4G to 5G teleCommunication system, future SAR will be required to share the spectrum with more systems than ever. However, current state-of-the-art Radio Frequency Interference (RFI) detection and suppression methods are mostly based on the spec-trum analysis, which survey the amplitude of the signals in the frequency domain and extract anomalistic signals from backscattered echoes. In order to enable further dynamic and coherent RFI detection and suppression, we performed a polarimetric analysis of SAR raw data and classification of the interfering signals in polarimetric domain.

This study conducted simultaneous experiments using a flood experiment field that can reproduce the conditions of flooded buildings, along with satellite monitoring using the L-band synthetic aperture radar (SAR) aboard the Advanced Land Observing Satellite-2 (ALOS-2). Through the experiments, we investigated the relationship between the multi-look number of interferometric processing, flooded building detection using interferometric coherence, and floodwater depth. For a better interpretation of the experiment results, we also performed a theoretical coherence simulation. Our results revealed that 1) coherence statistically depends on the multi-look number, 2) coherence-based change detection method can classify a flooded building with 6 cm or less water depth, and 3) there is no clear correlation between coherence degradation and floodwater depth. These findings will be useful for robust flood-area estimation of actual urban flooding events.

Mobile communications are often affected by various fading phenomena. Since channel state information (CSI), important to overcome channel fading, is always changing over time, channel prediction is necessary to know the correct CSI. Current channel prediction methods are mainly based on the amplitude and phase of the received signal. However, because the polarization state of the propagating wave also changes with time, the polarization mismatch problem occurs between an arriving wave and a receiving antenna, resulting in lower communication quality. In this paper, we propose the prediction of polarization states by using a quaternion neural network (QNN), which makes full use of the geometric properties of polarization states represented on the Poincare sphere to achieve a high-precision prediction. The experimental results show that, by predicting the polarization state, we can obtain more accurate CSI and a lower bit error rate.

This paper proposes model-based homogeneity (MBH) to extend compressed sensing (CS) for landmine-detection ground penetrating radar (GPR). Conventional CS methods have difficulty in distinguishing landmines from clutter since it principally pays attention to signal magnitude. In contrast, our method visualizes landmines based on homogeneity of high-dimensional scattering features in a spatial model. It realizes both the exclusion of clutter and the reduction of measurement points. Experiments demonstrate that the total measurement and processing time is reduced to one-twentieth of a conventional dense measurement case. We also investigate the influence of model size and number of landmines on the performance. The proposed method is capable of visualizing any objects having respective shapes by configuring corresponding models.

In this paper, we propose complex-valued reservoir computing (CVRC) to deal with complex-valued images in interferometric SAR (InSAR). We conducted classification of land forms by dealing with the interferogram data as sequential pixel values with nonlinearity in the complex domain.

This paper proposes a quaternion convolutional neural network (QCNN) for PolSAR land classification. The QCNN learns spatial features of polarization with quaternion convo-lutional layers. The QCNN learns relationship between components of input three-dimensional vectors. This property also makes the QCNN map inputs to space of higher dimension with the same number of parameters than a real-valued CNN (RVCNN). In our experiments, the QCNN shows better classification performance than conventional networks. We also present visually that the quaternion kernels extract spatial features by quaternionic convolution.

In this paper, we report the emulation of a line-of-sight displacement observed from the past SAR satellite. Recent SAR satellites can observe the same place from multiple tracks and estimate the ground displacement three-dimensionally from interferograms. We re-project the estimated displacement to the line-of-sight displacement observed from the past SAR satellite in order to compare the current and past ground events directly without external data and models. We present the experimental results to show the applicability of the proposal.

Recently synthetic aperture radar (SAR) has become implemented in multi-receiver systems to achieve, for example, along track interferometry (ATI) and/or digital beam forming (DBF) for wide observation swath. In this paper, we propose a novel radio frequency interference (RFI) detection method especially for multi-receiver SAR systems. We assume that RFI signals have greater similarity than the summation of radar echoes among multiple receivers. Based on this idea, we performed an interferometric analysis of the SAR raw data in both time and frequency domain and removed signals which associated with an exceptionally high correlation. We report experimental results with fully-polarimetric, single-pass interferometric dataset acquired by DLR's F-SAR sensor.

Future spaceborne SAR satellites are going to handle multiple receivers to take both high resolution and wide observation swath. In addition, they will transmit multiple chirp patterns to reduce the effect of the range ambiguity. Such a multi-channel and multi-waveform system raises additional azimuth ambiguities caused by the phase distortion of the received pulse. The phase distortion is a nonlinear effect and, the number of the channels and waveforms directly affect the processing results as if the pulse repetition frequency (PRF) is lower than it should be. In order to solve this problem, we propose to detect the aliased signal analytically in the Doppler frequency domain. Simulation results showed the suppression of the peak for 5 to 17 dB.

Complex-valued convolutional neural networks discover and/or adaptively classify local features in interferograms very effectively in interferometric synthetic aperture radar (InSAR). In this paper, we investigate the influence of label errors in teacher images on the classification performance. We find that performance is not affected so much from teacher-label errors as much as 10% or more. We also analyze the error characteristics experimentally.

In a complex-valued convolutional neural network, its elementary unit consists of a complex-valued convolution layer and a complex pooling layer. The pooling layer has a variety in its dynamics. In this paper, we propose complex absolute-value max pooling to extract complex-amplitude feature patterns meaningful for discovery and/or adaptive classification of land form in interferometric synthetic aperture radar (InSAR). Experimental examination into amplitude and phase values in convolutional kernels reveals that useful land-shape features emerge through self-organization in high-magnitude kernels, which suggests that the proposed dynamics is successful in extracting important features.

In this paper, we report the results of the experimental analysis observing the actual pixel variation properties in PolSAR data having various resolutions. Present PolSAR has reached a decimeter-level high resolution. In general, the resolution of PolSAR data is lowered down to 10m-20m in the real space by performing the multi-look process to reduce noise in the pixel values widely for land classification. However, lowering resolution prevents us from discovering new land classes potentially enabled by the resolution enhancement. Through our experiments, we aim to confirm whether it is really meaningful to utilize the respective pixel signals of high-resolution PolSAR data for land classification without any lowering resolution. Although the main cause of the pixel variation occurring in the actual PolSAR data has not been elucidated yet in this letter, we would like to show this experimental results as a material for discussion.

Toward 6 dimensional synthetic aperture radar (SAR), i.e., the combination of three dimensional SAR tomography (To-moSAR) and three dimensional differential SAR interferometry (3DInSAR), continuous three dimensional deformation monitoring using SAR is an arising theme. In this paper, we report initial results using the dataset for the active volcano in the Pacific Ocean, Nishinoshima. We observed the island frequently for three months from three identical orbits including left observation and recovered three dimensional deformation of the volcanic island continuously. Experimental results revealed several small events in the volcano and showed the possibility of the method.

Future spaceborne SAR satellites will achieve both high resolution and wide swath with their multiple receivers. In order to suppress range ambiguities, cyclic up and down chirp and azimuth phase coding have been widely applied. A multiple receiver system has lower pulse repetition frequency to increase the swath width and thus, the effect of azimuth phase coding to mitigate range ambiguities is decreased. In this paper, we reveal the possible combination of the two methods to overcome this drawback.

When we process PolSAR data by machine learning to analyze vegetation, it is necessary to know how polarization changes by scattering. In order to clarify the physical mechanisms of scattering in PolInSAR (polarimetric interferometric synthetic aperture radar), we examine how polarization changes by PPO-BD (Pixel-by-Pixel Optimization considering Baseline Difference), which is an adaptive filter of InSAR images. By using scattering sphere we propose, we find that the features of the scattering mechanisms are enhanced after polarization is optimized by PPO-BD. It means that PPO-BD performs the phase optimization by taking into account the differences in the scattering mechanisms among pixels. We find that, when PPO-BD optimizes the phase to reduce singular points by utilizing polarimetric data, it also emphasizes the polarimetric mechanism information pixel by pixel.

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.

For urban flood mapping, building orientation is one of the most important parameters mainly considering amplitude images of satellite synthetic aperture radar (SAR). This study investigated the influence of the orientation angle of a building on double bounce effect from the floodwater surface. We suggest the potential key component of urban flood detection considering the characterization of the double bounce effect from land surface backscatters in the selected experimental area, which covers the hardest flood-hit municipalities during the 2015 Kinu River flood in Japan. To improve the urban flood detection ability, the correlation coefficient of the intensity derived from the building orientation was then applied to detect the flooded buildings.

Synthetic aperture radar (SAR) shares its radio frequency with the other systems and thus, inevitably receives their signals. Such cross-talk, radio frequency interference (RFI) has been traditionally eliminated by spectrum filters. However, a latest L-band SAR, ALOS-2, receives frequency modulated wideband signals that cannot be detected by traditional spectrum filters. These RFIs have the same or wider bandwidth than SAR and thus, appears in SAR images. They also interrupt interferometric (InSAR) and polarimetric (PoISAR) analysis because it appears in every observations and in full-polarimetric mode images. We report the investigation results of the profiles of such RFI in Tokyo bay, Japan.

In this paper, we examine the potential of interferometric analysis using L-band synthetic aperture radar (SAR) for flood monitoring. The integration of amplitude and interferometric coherence is one of the novel methods for flood monitoring in urban area. However, its accuracy has not been evaluated especially in case of L-band SAR data. Here, we used the 2015 September flood in Joso city, Ibaraki prefecture, Japan for the evaluation of the latest L-band SAR satellite ALOS-2 PALSAR-2.

Interferograms obtained in interferometric synthetic aperture radar (InSAR) often suffer from decorrelation and singular points (SPs) originating from thermal noise and interference. To analyze the phenomenon, first, this paper presents the results of scaled optical experiment free from thermal noise, where the SP origin is interference. We find that the amplitude of the SP-constructing pixels, namely, singular unit, and of nearby pixels is lower than that of other pixels. This amplitude reduction is enhanced by multilooking process. These results suggest that the number of effective scatterers in a single pixel has reduced to such an extent that individual interference has become visible. We also conduct the same analysis on the SAR data. We find that plain areas show the same features as the optical experiment, implying the same mechanisms of SP generation. In contrast, sea areas present no localization, indicating thermal noise in electronics as the major reason. It is widely known that interference among many incoherent scattered waves presents Rayleigh or similar distribution in its amplitude as a result of central limit theorem. As the number of scatterers reduces, the amplitude becomes log-normal or other distribution. However, no analysis was reported on the local properties in such a case that the central limit theorem does not hold. Investigation of such local properties will also be useful in designing SP filters. The significance of noncentral-limit-theorem situations will increase its importance in the use of SAR data, of which resolution becomes further higher in the near future.

In this paper, we propose two methods to optimize scattering mechanism vectors in polarimetric interferometric synthetic aperture radar (InSAR) dealing with high-resolution SAR data. The methods optimize an interferogram by focusing on the pixel-by-pixel variation of the scattering mechanisms. We show the efficacy of the methods using fully polarimetric interferometric radar data obtained from the Advanced Land Observing Satellite-2 PALSAR-2 to generate accurate digital elevation models successfully. Finally, we confirm the importance of considering the polarization in such InSAR systems by comparing parameters optimized in the proposed methods with those calculated from the well-known Pauli coherency matrix.

This paper reports the polarimetric characteristics of temporarily coherent radio frequency interference (RFI) observed by ALOS-2 PALSAR-2. The RFI has high interferometric coherence as if it is a backscattered signal from the ground. However, its radio frequency band and the Doppler frequency are different from the backscattered signal. The RFI, namely, intermittently transmitted wideband (ITWB) RFI, effects both interferometric and polarimetric analysis, because it has high temporal interferometric and polarimetric coherency. The detection and removal scheme for the ITWV RFI is also discussed in this paper.

In 2016, the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2”) observed various disaster affected areas. Japan Aerospace Exploration Agency (JAXA) operated the emergency observation more than hundred times in the year. The Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard ALOS-2 contributed for detecting the disaster affected area, ground deformation and flood affected area. Especially for the ground deformation and damaged area detection caused by the devastating earthquakes in 2016, e.g., Kumamoto earthquakes in Japan and Kaikoura earthquake in New Zealand, researchers provided variable analytical results from ALOS-2 observation data. In this paper, some examples of the emergency observation results are presented.

The Advanced Land Observing Satellite-2 (ALOS-2) was successfully launched on 24th May, 2014. The mission sensor of ALOS-2 is the Phased Array type L-band Synthetic Aperture Radar-2 called PALSAR-2 which is the state of the art L-band SAR system. At After launch, the initial checkout and the calibration and validation phase had been completed, and the PALSAR-2 standard products were released via web site at the end of November 2015. Until now, ALOS-2 has had contributed to a lot of emergency observations for disasters such as earthquakes flood, land slide which were impacted by typhoons, and volcano eruptions, not only in Japan but also in the world. Furthermore, based on the Basic Observation Scenario (BOS) of ALOS-2, base map data are collected and archived for the interferometry SAR processing in Japan area as well as global world. This document describes the results of ALOS-2 operation in routine operation phase.

One of the main missions of the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2” ) is the disaster monitoring. Japan Aerospace Exploration Agency (JAXA) has operated the emergency observation more than hundred times in 2015. Not only the most important event in 2015, the Mw 7.8 Gorkha earthquake on April 25, the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard ALOS-2 observed various floods, volcano eruptions and earthquakes. In this paper, we present some emergency observation results which were impossible to be performed by the previous ALOS. That is, automatically burst aligned ScanSAR to ScanSAR interferometry and, left / right looking for increasing acquisition opportunity.

In this paper, we report a radio frequency interference (RFI) detection method which is exclusively sensitive to the temporally pulsed wide-band signal. We use local autocorrelation in the range-time azimuth-frequency domain instead of typical range-frequency azimuth-time domain. Traditional RFI detectors assume that RFI have time-varying wide-band (TVWB) and / or time-stationary narrow-band (TSNB) features. The proposed method assumes that there is another type of RFI, namely, intermittently transmitted wide-band RFI. This kind of RFI superimposes on the SLC image however, its short pulse duration hinders us to detect it in range-frequency azimuth-time domain. Contrarily, in range-time azimuth-frequency domain, this kind of RFI can be detected easily. Here, we present a basic methodology and experimental results.

An experiment to examine the detectability of volcanic ash cover and its thickness by using L-band synthetic aperture radar (SAR) was carried out. Test sites with 0, 10, 20, and 40 cm ash layers were developed. PALSAR-2 and Pi-SAR-L2 observations were carried out several times with and without the ash cover. The PALSAR-2 backscatter coefficients in HH polarization, σHH0, for soil with ash layers are on average 1.7 to 3.5 dB lower than that for soil without ash layers. On the other hand, the 0 cm site shows almost the same value throughout the experiment. The ash layer was also measured by ground based radar operated in C-band. The reflectivity-frequency plot obtained from the soil with 2 to 6 cm ash layers appears as a sine-like curve, indicating the reflection from two layers (soil plus ash). It indicates that no linear correlation is expected between σ0and thickness of ash layer. Correlations between the ash thickness and representative parameters obtained from L-band SAR observation are examined. It indicates that volume scattering component shows the highest correlation with R2of 0.7143.

In principle, we can construct highly precise digital elevation model (DEM) with interferometric synthetic aperture radar (InSAR) observations. However, in actual observed images, we find so many rotaional points, namely, phase singular points (SPs), that we cannot determine the height accurately. It is significantly important to investigate the SP phenomenon. Presently the origin of the SPs is not entirely clear. In this paper, we design a scaled experimental setup in optics to elucidate the mechanisms of the SP generation. From the results, we confirm that low amplitude pixels (dark speckles) are emphasized by multilook process, and that many of these pixels are singular unit pixels. The multilook process is equivalent to interference of multiple complex-amplitude values. In addition, a similar interference is considered to occur in a single pixel of a single-look SAR image. It is suggested that interference of waves from multiple scattering sources in a single pixel can be one of the causes of SP generation.

In the field of Synthetic Aperture Radar (SAR) interferometry, we generally expect that there is only unique dominant scatterer in one pixel. To make a SAR interferogram, we have to observe a place twice. The dominant scatterer in every pixel must be identical in those observations. However, in an actual situation, there are multiple scatterers in one pixel physically, and the dominant one may be changed in the two observations. Slight difference of incident angle may cause the change of the dominant scatterer and the propagation path. The change can generate singular points (SPs) in the inter-ferogram which prevent us from accurate phase unwrapping. Here, we present experimental results in an anechoic chamber which show the mechanism of the singular point generation.

In Synthetic Aperture Radar (SAR) interferometry(InSAR), we generally expect that there is only one unique dominant scatterer in a pixel. To make a SAR interferogram, we have to observe every place twice. Between the observations, the dominant scatterer is expected to unchange. However, in actual situations, there are multiple scatterers in one pixel, and a dominant one may change observation by observation. Our main idea in this paper is that this change happens more frequently than we have expected, and that this phenomenon can generate singular points (SPs) in the SAR interferogram which prevent us from accurate phase unwrapping. Here, we present the results of the preliminary experiments using real aperture radar system, which suggest one of the mechanisms of the singular point generation.

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.

SAR interferogram requires a robust filtering method for generation of appropriate digital elevation model (DEM).We previously proposed a filtering method based on complex-valued Markov random field (CMRF) model. The CMRF filter estimates the pixel values which generate the singular point (SP) and by use of the complex correlation between neighbor pixels. Since a complex number is represented by two real numbers, the complex-correlation learning (i.e., CMRF filter) is similar to the real-imaginary separate double-dimensional correlation learning (RI-MRF filter). In this paper, we compare the bases of their processing dynamics and the filtering accuracy between the CMRF and RI-MRF filters in experiment for ALOS data.

We evaluated our co-registration method which uses the number of SPs and amplitude information for ALOS data. Experimental results showed that our proposed method improves the quality of the interferogram as much as the case of JERS-1 data.

Correcting the phase ambiguity of synthetic aperture radar (SAR) interferogram is an important process to create an accurate digital elevation model (DEM) for analyzing the geometry. The ambiguity often appears as the rotational point in the phase map which are called singular points (SPs). One of the origins of the SPs is the local distortion of the master and the slave, the source of interferogram. To solve this problem, we previously proposed a local co-registration method which employs the SPs as the evaluation criterion(SPEC method). In this paper, we propose an improved version of this co registration method. This version refers the amplitude in formation for shape-from-shading technique, which estimates the divergence of the range direction. We demonstrate the effectiveness of the improvement by comparing the DEMs generated from those interferograms.

There is increasing demand for landscape acquisition using interferometric synthetic aperture radar for frequent Earth observation. Making an accurate digital elevation model (DEM) from an interferogram is often seriously affected by a mass of singular points (SPs) included in the interferogram. The origin of a type of SPs lies in inaccurate coregistration in making the interferogram. However, there have not been any method more effective than affine transformation based on cross correlation. Numerous research works have aimed at how to process the SPs resulting in the coregistration. In this paper, contrarily, we propose an additional method of local and nonlinear coregistration which employs the number of SPs as the evaluation criterion. This method provides us with an interferogram to generate a more accurate DEM. Experiments demonstrate that, with the proposed method, we can obtain a DEM having higher signal-to-noise ratios.

Interferometric synthetic aperture radar (InSAR) is a key technology in geoscience. In the generation of a digital elevation map (DEM), the elimination of singular points (SPs) is the most important process besides the phase unwrapping (PU). A SP means a point where the phase rotation is not zero in the interferogram obtained by InSAR. What yields the SPs? One reason is a big cliff actually existing in the observation region. Empirically, such cliff-generated SP pairs (positive and negative SPs) are located at a distance from each other. Contrarily, other SP pairs, which make up the majority of the SPs, emerge closely to each other. Such close pairs arise from the autointerference caused by the diffraction in electromagnetic-wave propagation including the local permittivity fluctuation effect related to moisture vapor density in the air and other effects. We call the former the global SPs, while we do the latter the local SPs.