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The 39th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), held 28 July–2 August 2019 at the Pacifico Yokohama Convention Center, Japan, welcomed more than 2,500 participants from 56 countries and areas. The local organizing com-mittee (LOC) received positive feedback from many participants, who described the venue and conference as being very convenient, well organized, and most enjoyable. The general chair attributes these positive evaluations to strong LOC teamwork. The goal of pro-viding the “best experience for all participants,” which IEEE Geoscience and Remote Sensing Society (GRSS) President Paolo Gamba and I confirmed at the GRSS administrative committee meetings, was shared by all of the LOC members.One of the highlights of the symposium was the opening ceremony, which was held in a solemn but warm atmosphere on 29 July with Their Majesties the Emperor and Empress of Japan in attendance. In his address, His Majesty the Emperor referred to the importance of detailed and accurate data acqui-sition and analysis, which greatly encouraged the participants working in the geoscience and remote sensing field.In this column, the LOC cochairs, who supervised the harmonious teamwork, revisit the events and re-view the activities, statistics, and logistics, which will be helpful for future IGARSS events. Following each of the following sections, the chairs’ names are provided so that readers can contact them. The “Conference Re-port” column “IGARSS in Yokohama, Japan: Impres-sions From the First Days” in the September 2010 issue of IEEE Geoscience and Remote Sensing Magazine [1] i s also informative.

This paper presents vehicle occupant body imaging algorithms for detecting the presence of children that are left behind in vehicles. The proposed algorithms utilize occupancy grid mapping (OGM) techniques developed for autonomous driving, which are modified for in-cabin human detection purposes. Two different kinds of OGM techniques are presented to deal with two major difficulties in imaging the occupant bodies in an in-cabin environment. The processing flows of the proposed algorithms are detailed in this report. Two experimental results for 60 GHz millimeter-wave radars are also presented for performance verification.

A space-borne synthetic aperture radar (SAR) is widely used for maritime surveillance because it can observe a broad area. Although a low pulse repetition frequency (PRF) mode is generally selected to observe a wider area, it causes azimuth ambiguities that may lead to false detection and affect the accuracy of ship detection. In previous work, an azimuth ambiguity discrimination algorithm using the Doppler spectrum was presented. However, this algorithm did not eliminate false detection and presented challenges for ship detection performance. To achieve more precise detection, a novel algorithm using a compressive sensing (CS)-based SAR image is proposed. CS can reconstruct the ambiguity-suppressed SAR image, resulting in further improvement of azimuth ambiguity discrimination. The proposed algorithm was applied to a SAR image simulated by half-downsampling of the received signals. The evaluation shows that the proposed algorithm suppresses the power of azimuth ambiguity up to 15 dB and eliminates false detection. These findings demonstrate that the algorithm discriminates robustly and significantly improves ship detection performance.

We propose a novel SAR azimuth ambiguity suppression method. The proposed method is a post process for a SAR image; namely, it does not require any modification to the SAR image formation process nor the SAR system design. Noting that the azimuth ambiguity is blurred in the SAR image, the proposed algorithm refocuses the image to focus the azimuth ambiguity and then automatically detects and identifies the locations, and also the orders, of the ambiguities in the refocused image. After the detected ambiguities in the refocused image are eliminated, the final SAR image is obtained by refocusing back to the true image. Experimental results using true satellite images show that the proposed method successfully suppresses the ambiguities without causing severe degradations to the true images.

Compressive sensing (CS) is based on the random coding theorem. In this term, lowness of the coherence of forward/backward transformation matrix is important. Random sampling makes coherence low, then conventional algorithms are based on random sampling. A novel idea to get the low coherency of ambiguity is not random sampling but simple decimation of the SAR azimuth arrays. To make simple decimated array, the SAR system only needs to set low pulse repetition frequency (PRF). In this paper, we have demonstrated our method of compressive sensing for synthetic aperture radar (SAR) is effective even in the case of sub-Nyquist linearly decimated array.

Polarimetric radar provides useful information on scattering mechanisms, and advances in polarimetric synthetic aperture radar (POLSAR) technology have shown the effect of polarization information on various applications. The disadvantage of POLSAR is low azimuth resolution capability. Since the effective pulse repetition frequency (PRF) in each polarimetric channel of POLSAR is half the total PRF, the resolution is twice as low as the single-polarization SAR. This drawback may be mitigated by employing a superresolution method designed for polarimetric radar such as polarimetric bandwidth extrapolation (PBWE), previously proposed by the authors. Thus far, the authors only empirically showed that polarization information helps in improving resolution via numerical simulations. In this paper, theoretical analysis on the resolution of a polarimetric radar is provided. Along with the resolution, estimation accuracy of the polarization property of the target is studied. In the analysis, we employ the recently proposed metric called the statistical resolution limit (SRL) that provides the highest resolution achievable by any unbiased parametric spectral estimator. The SRL of polarimetric radar is derived and compared with that of single-polarization radar. The Cramer-Rao bound (CRB) of the polarization estimation error of the polarimetric radar is also derived. The complexified full Fisher information matrix and the CRBs of signal parameters are derived first for the general multichannel signal in nonwhite Gaussian noise. Then, the SRL and the polarization estimation error are derived using the CRBs of signal parameters for the cases where two point targets are closely located. It is shown analytically and numerically that the polarization information helps in improving the SRL. It is also shown that the polarimetric processing significantly improves the accuracy of the polarization estimation when two targets are located closer than the Fourier resolution cell. Finally, the performance of the PBWE is evaluated via simulations and is compared to the SRL and the CRB for the polarization estimation.

Inverse synthetic aperture radar (ISAR) is one of the radar techniques used to observe 2-D images of a remotely based target using radio waves. If we keep observing the target and consecutively generate multiple ISAR images, which we call an ISAR movie, the target image varies considerably due to the motion of the target. The authors have proposed an algorithm for reconstructing a 3-D target shape from an ISAR movie; however, the algorithm requires a priori knowledge of the relative motion of the target. In this paper, we propose a novel method that estimates the relative motion and the 3-D shape of the target using a multistatic ISAR movie.

The resolution of a synthetic aperture radar (SAR) image, in range and azimuth, is determined by the transmitted bandwidth and the synthetic aperture length, respectively. Various superresolution techniques for improving resolution have been proposed, and we have proposed an algorithm that we call polarimetric bandwidth extrapolation (PBWE). To apply PBWE to a radar image, one needs to first apply PBWE in the range direction and then in the azimuth direction, or vice versa . In this paper, PBWE is further extended to the 2-D case. This extended case (2D-PBWE) utilizes a 2-D polarimetric linear prediction model and expands the spatial frequency bandwidth in range and azimuth directions simultaneously. The performance of the 2D-PBWE is shown through a simulated radar image and a real polarimetric SAR image

For urban area monitoring, detection and classification of small targets with synthetic aperture radar imagery are one of the useful solutions. The conventional method has the problem that when a target on both master and slave images at the same position has the same radar cross section, it does not classify the target as either a slightly moved reparked vehicle or as a different vehicle. To overcome this problem, we propose a Fourier-transform-based simple phase compensation method for both accurate coherence estimation and classification of the reparked vehicle's status. The proposed method obtains the maximum value of the frequency basis, and also determines the threshold, which is based on effective sample number, with a constant false alarm rate. This number is determined by the peak signal-to-noise ratio in estimation window, and are used on the preprocessed probability density function for each sample number. Finally, the proposed method classifies a target as either a slightly moved vehicle or as a different vehicle by using both the proposed coherence and threshold detection, which enables it to determine the target status. Experimental results with the COSMO-SkyMed X-band spotlight SAR images demonstrate the effectiveness of the proposed method.

When vehicles and ships are observed by radar, echoes from the target are buried in the echoes (clutter) from the ground and the sea surface in the background and detection becomes difficult. Previously, a method known as a Polarimetric Whitening Filter (PWF) was proposed as a target detection technique that focuses on the difference in the polarizations of the clutter and the target and verified its effectiveness. In addition, the authors proposed the Polarimetric Notch Filter (PNF) as a clutter suppression scheme in an environment with a low signal-to-clutter power ratio (SCR) and verified the achievement of superior improvement in the SCR performance. In this paper, we propose a target detection algorithm that obtains a constant probability of false alarm by whitening the polarization characteristic of the residual clutter power after suppressing the clutter power by the PNF. From the results of evaluations by computer simulation, we see that the SCR improvement factor of the proposed method was on average about 1.5 dB better than conventional PWF, but the target detection probability was nearly identical. In addition, the results of the performance evaluation of the polarization data of the noise of asphalt and vehicles acquired by polarimetric FMCW radar in the 95-GHz band where the SCR improvement factors of PNF and PWF were 7.44 and 5.85 dB, respectively, and the SCR improvement factor of PNF was 1.59 dB better. Although the target detection probability was somewhat higher for PNF than PWF, they were nearly identical. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 88(3): 33–43, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.20158

The remote sensing Synthetic using Aperture Radar can obtain the information of moving targets on the earth surface using MTI (Moving Target Indication) technique, which detects differentiation of the surface image data obtained in successive sessions taken with two antennas mounted on one satellite or two satellites flying in a close formation. In the latter case, the operation of the differentiation is usually performed at the ground station, then each satellite needs to transmit the large amount of data to the ground station separately, which requires broad downlink bandwidths. However, the earth surface image data at the two satellites clearly have a strong correlation since the static background image data other than the moving targets are essentially the same except for some noise factors. Focusing on this fact, we claim that the transmission data can and should be reduced by taking off the redundancy of the two data and sending only the differential parts. In this paper, we discuss the coding theory matter related to this redundancy suppression first, and then discuss source, channel and network coding, and also the Slepian-Wolf theorem for two correlated sources. We next propose a coding and compressing scheme for a certain simplified model. We further generalize it in order to apply this idea to a more realistic model and present a procedure of the proposed scheme.

This paper presents an experimental result to demonstrate azimuth ambiguity suppression with the displaced phase center antenna technique that is promising technique to realize the high resolution wide swath imagery for spaceborne synthetic aperture radar (SAR). The dual receiver antenna data was acquired by airborne Ku-band SAR. In this paper, we propose a signal compensation procedure to cope with beam squint for the following coherent signal processing such as the dual channel reconstruction algorithm. The azimuth ambiguity suppression capability is evaluated by comparing the image reconstructed from dual-receiver channel to the single receiver channel image. The experimental result shows significant azimuth ambiguity suppression.

Along track interferometric (ATI) synthetic aperture radar (SAR) has the capability of detecting moving targets and measuring the line-of-sight velocity of the target. With the ATI SAR, the current velocity map can be obtained by combining two perpendicular ATI data sets recorded over sea in rapid succession. In this paper, we describe how sea surface current can be mapped using airborne Ku-band SAR and present results of sea surface current at Cape Irago in Japan.

Statistical resolution limit (SRL) is a simple metric that provides the highest resolution achievable by any unbiased parametric spectral estimator. In this paper, the SRL of polarimetric radar is derived and compared with that of single polarization radar. The SRL is defined using the Cramer-Rao bound (CRB) of signal parameters for the cases where two point targets are closely located. It is shown analytically and numerically that the polarization information helps improving the SRL.

Synthetic Aperture Radar (SAR) often suffers from interference signal from various radio sources. In general, notch filters or band elimination filters have been utilized to eliminate such interference signal; however, if the bandwidth of the interference signal is relatively wide, the gap in the spectrum caused by the band elimination filter could significantly degrade the original image. We propose an algorithm to suppress relatively wide bandwidth interference while maintaining the image quality. In the algorithm, spatially variant interference suppression filter is generated based on the signal of interference free band, then the filter is applied to the interference contaminated image. Unlike the band elimination filter, the spatially variant interference suppression filter preserves the signal component within the interference contaminated band; therefore, the image distortion caused by the spectrum gap can be largely eliminated. The algorithm has been tested with a simulated interference contaminated image generated from the real 10cm resolution airborne Ku band SAR image and a TerraSAR-X image. It has been shown that while conventional band elimination filter would degrade the image quality, the image quality of the interference suppressed image obtained by the proposed algorithm is satisfactory.

Passive bistatic radars (PBRs) exploit existing transmitters such as TV broadcasts as the source of illumination. PBR is consisted of two receivers, with one antenna pointed at the source and the other at the target, and the target range is determined by correlating the signal scattered by the target with the signal directly arrived at the receiver. Since PBR does not transmit any waveform, it consumes lower power, and no frequency allocation is required. We have conducted a field experiment exploiting terrestrial digital TV broadcast to assess the feasibility of ISAR (Inverse Synthetic Aperture Radar) observation based on PBR. The wide and flat spectrum of the OFDM signal and the small gap between the channels enables us to obtain high range resolution of up to several meters by exploiting multiple physical channels. Therefore, moderate resolution ISAR imaging is expected to be possible. In this paper, PBR based ISAR algorithm is briefly explained, and the first example of observed PBR based ISAR image of an aircraft target is shown.

Resolution of a radar is limited in range by its bandwidth. One of the various super-resolution techniques for improving its resolution is bandwidth extrapolation (BWE). In the technique, a linear prediction model is fitted to the data, and the model is used to extrapolate the bandwidth. In this paper, the concept of BWE is extended, and a new algorithm called polarimetric bandwidth extrapolation (PBWE) applicable to polarimetric radar data is proposed. It is shown through numerical simulations that utilization of full polarization information allows PBWE to improve the resolution beyond the conventional BWE method. Some results of physical simulation experiment using a W-band polarimetric FMCW radar and corner reflectors are shown to confirm the advantage of PBWE.