Single-channel SAR Images Research Articles

Despeckling Polarimetric SAR Data Using a Multistream Complex-Valued Fully Convolutional Network

A Polarimetric Synthetic Aperture Radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterization. PolSAR images are inherently affected by speckle. Therefore, before deriving ad hoc products from the data, the polarimetric covariance matrix needs to be estimated by reducing speckle. In recent years, deep learning based despeckling methods have started to evolve from single channel SAR images to PolSAR images. To this aim, deep learning based approaches separate the real and imaginary components of the complex-valued covariance matrix and use them as independent channels in a standard convolutional neural networks. However, this approach neglects the mathematical relationship that exists between the real and imaginary components, resulting in sub-optimal output. Here, we propose a multi-stream complex-valued fully convolutional network to reduce speckle and effectively estimate the PolSAR covariance matrix. To evaluate the performance of CV-deSpeckNet, we used Sentinel-1 dual polarimetric SAR images to compare against its real-valued counterpart, that separates the real and imaginary parts of the complex covariance matrix. CV-deSpeckNet was also compared against the state of the art PolSAR despeckling methods. The results show CV-deSpeckNet was able to be trained with a fewer number of samples, has a higher generalization capability and resulted in a higher accuracy than its real-valued counterpart and state-of-the-art PolSAR despeckling methods. These results showcase the potential of complex-valued deep learning for PolSAR despeckling.

미분을 이용한 단일채널 SAR SLC 영상 내 지상 이동물체의 탐지방법

SAR를 이용한 지상이동물체탐지(GMTI)는 SAR의 주요 활용 기술 중 하나이다. 최근 위성 탑재 SAR 시스템의 해상도가 높아지면서 지상이동목표물 탐지의 유용성은 더욱 강조되고 있다. 현재까지 다양한 지상이동물체탐지 기법이 개발되었으나 대부분은 다중채널 SAR 시스템을 이용하는 기술에 집중되었다. 그러나, 아직도 단일채널 SAR 영상으로부터 지상 이동물체를 탐지하는 것은 매우 어려운 문제로 남아 있는 반면 다중채널 위성 탑재 SAR 시스템은 아직은 그 활용이 현실적으로 매우 제한적인 상황이다. 일단 지상의 목표물이 탐지되고 이동속도가 3 m/s(약 10.8 km/h) 이상인 경우 그 목표물의 이동속도는 단일채널 SAR 자료라도 오차범위 약 5%의 정밀도로 복원 가능하다. 따라서 단일채널 SAR 자료로부터 지상의 이동물체 자체를 탐지하는 것이 핵심이며, 이 논문에서는 SAR Single-Look Complex(SLC) 영상자료에 미분을 적용하여 쉽고 빠르게 탐지하는 방법을 제시한다. 이 논문에서는 SAR SLC 자료의 미분 값은 도플러 중심주파수를 나타냄을 유도하고, 따라서 미분 값은 지상이동물체 탐지에 매우 효과적임을 설명하고자 한다. 이 논문에서 제시하는 미분 방법의 결과와 정밀한 속도복원 방법의 상관계수 <TEX>$R^2$</TEX> 는 0.62로 나타났으며, 이는 이동물체를 탐지하는 데는 충분함을 지시한다. 이 방법은 매우 단순한 미분으로 도플러 중심주파수 분석에 근거하고 있으나 최종 자료처리에 앞서 도플러 경사도를 제거해야 하며, 적용결과의 효율성과 신뢰도는 이 도플러 경사도 제거 과정에 크게 좌우된다. 지상에 모서리 산란체를 탑재하고 이동속도를 조절한 실험용 차량과 이를 관측한 TerraSAR-X SLC 자료를 이용하여 검증을 실시하였다. 검증결과 지상 이동물체를 매우 쉽게 탐지하면서도 정지된 상태의 강한 산란체는 약 18.5 dB의 신호파워를 줄여 효과적으로 제거 하는 것으로 나타났다. 현재 이 방법은 지상의 이동속도 8.8 km/h 이상인 경우 매우 효과적이며, 아리랑-5호를 비롯한 모든 단일채널 SAR 시스템에 적용 가능하다. SAR ground moving target indicator (GMTI) has long been an important issue for SAR advanced applications. As spatial resolution of space-borne SAR system has been significantly improved recently, the GMTI becomes a very useful tool. Various GMTI techniques have been developed particularly using multi-channel SAR systems. It is, however, still problematic to detect ground moving targets within single channel SAR images while it is not practical to access high resolution multi-channel space-borne SAR systems. Once a ground moving target is detected, it is possible to retrieve twodimensional velocities of the target from single channel space-borne SAR with an accuracy of about 5 % if moving faster than 3 m/s. This paper presents a quick-and-dirty method for detecting ground moving targets from single channel SAR single-look complex (SLC) images by differentiation. Since the signal powers of derivatives present Doppler centroid and rate, it is very efficient and effective for detection of non-stationary targets. The derivatives correlate well with velocities retrieved by a precise method with a correlation coefficient <TEX>$R^2$</TEX> of 0.62, which is well enough to detect the ground moving targets. While the approach is theoretically straightforward, it is necessary to remove the effects of residual Doppler rate before finalizing the ground moving target candidates. The confidence level of results largely depends on the efficiency and effectiveness of the residual Doppler rate removal method. Application results using TerraSAR-X and truck-mounted corner reflectors validated the efficiency of the method. While the derivatives of moving targets remain easily detectable, the signal energy of stationary corner reflectors was suppressed by about 18.5 dB. It results in an easy detection of ground targets moving faster than 8.8 km/h. The proposed method is applicable to any high resolution single channel SAR systems including KOMPSAT-5.

Glacier Velocity Estimation by Means of a Polarimetric Similarity Measure

The contribution of polarimetric synthetic aperture radar (PolSAR) images compared with that of single-channel SAR images in terms of temporal scene characterization has been found and described to add valuable information in the literature. However, despite a number of recent studies focusing on single-polarized glacier monitoring, the potential of polarimetry to estimate the surface velocity of glaciers has not been explored due to the complex mechanism of polarization through glacier/snow. In this paper, a new approach to the problem of monitoring glacier surface velocity is proposed by means of temporal PolSAR images, using a basic concept from information theory, i.e., mutual information (MI). The proposed polarimetric tracking method applies the MI to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximum if the images are geometrically aligned. Since the proposed polarimetric tracking method is very powerful and general, it can be implemented into any kind of multivariate remote sensing data such as multichannel optical and single-channel SAR images. The proposed polarimetric tracking is then used to retrieve the surface velocity of the Aletsch Glacier in Switzerland and the Inylchek Glacier in Kyrgyzstan with two different SAR sensors: the Experimental SAR airborne L-band (fully polarimetric) and Envisat C-band (single-polarized) systems, respectively. The effect of the number of channels (polarimetry) into tracking investigations demonstrated that the presence of snow, as expected, affects the location of the phase center in different polarization and frequency channels, as for the glacier tracking with temporal HH compared to temporal VV channels. In this paper, it is shown how it is possible to optimize these two different contributions, considering the multichannel SAR statistics.

GLACIER SURFACE MONITORING BY MAXIMIZING MUTUAL INFORMATION

Abstract. The contribution of Polarimetric Synthetic Aperture Radar (PolSAR) images compared with the single-channel SAR in terms of temporal scene characterization has been found and described to add valuable information in the literature. However, despite a number of recent studies focusing on single polarized glacier monitoring, the potential of polarimetry to estimate the surface velocity of glaciers has not been explored due to the complex mechanism of polarization through glacier/snow. In this paper, a new approach to the problem of monitoring glacier surface velocity is proposed by means of temporal PolSAR images, using a basic concept from information theory: Mutual Information (MI). The proposed polarimetric tracking method applies the MI to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximal if the images are geometrically aligned. Since the proposed polarimetric tracking method is very powerful and general, it can be implemented into any kind of multivariate remote sensing data such as multi-spectral optical and single-channel SAR images. The proposed polarimetric tracking is then used to retrieve surface velocity of Aletsch glacier located in Switzerland and of Inyltshik glacier in Kyrgyzstan with two different SAR sensors; Envisat C-band (single polarized) and DLR airborne L-band (fully polarimetric) systems, respectively. The effect of number of channel (polarimetry) into tracking investigations demonstrated that the presence of snow, as expected, effects the location of the phase center in different polarization, such as glacier tracking with temporal HH compared to temporal VV channels. Shortly, a change in polarimetric signature of the scatterer can change the phase center, causing a question of how much of what I am observing is motion then penetration. In this paper, it is shown that considering the multi-channel SAR statistics, it is possible to optimize the separate these contributions.

Segmentation-based joint classification of SAR and optical images

The authors devise a new data fusion technique for classification that exploits the information contained in single-channel SAR and optical images optimally. In fact, even though good classification performance can be achieved by using multiple optical channels, it is shown that classification results from a single optical channel are not appreciably better than those obtained with a single SAR image. The impact of fusion of SAR and optical images is investigated quantitatively. To characterise the limits that can be achieved, both lower and upper bounds to classification performance are introduced, corresponding respectively to single-pixel classification and joint classification of all pixels in the regions defined by the ground truth. First, an optimised technique for single-channel image segmentation followed by maximum likelihood (ML) classification is proposed. A significant performance improvement is demonstrated by classifying the homogeneous regions identified by segmentation, instead of single pixels or even small windows of 3×3 pixels. Indeed, the result consistently approaches the upper bound. Next, the proposed ML segmentation technique is extended to exploit the information available in SAR and optical images jointly to define the best set of segments. The segmented SAR and optical images are then used together to obtain the best possible classification of the segments, yielding a significant performance improvement with respect to using either sensor alone. Classification performance is again significantly better than with single pixels, or small windows, and approaches the upper bound.