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Practical Multichannel SAR Imaging in the Maritime Environment


The U.S. Naval Research Laboratory (NRL) recently developed an X-band airborne multichannel synthetic aperture radar (MSAR) test bed system that consists of 32 along-track phase centers. This system was deployed in September 2014 and again in October 2015 to perform extensive and systematic data collections on a variety of land and maritime scenes under different environmental conditions. This paper presents a detailed experimental analysis of imaging in the maritime domain using data captured by the NRL MSAR system. After presenting some of the important details of our NRL MSAR system, we demonstrate velocity-based imaging of a variety of moving backscatter sources including ships and shoaling ocean waves. Our analysis is based on the velocity SAR (VSAR) technique, which was originally conceived by Friedlander and Porat. Practical application of this algorithm in the maritime domain requires a number of pre- and postprocessing stages, which are described here in detail. Our results are then benchmarked against the traditional along-track interferometry, where it is demonstrated that VSAR processing is better able to correctly compensate motion-induced distortion.

Analysis of SAR images speckle reduction techniques


Remote Sensing is the process to acquire information about phenomena or an object without physical contact with the subject under consideration. The applications of remote sensing vary from Geography to land surveying and many more. Optical sensors used reflected sun rays for object identification, being usable in day light and shorter wavelength makes it inefficient for limited time availability. Thus it cannot penetrate the earths’ surface and is affected by atmospheric factors as well. Radar based remote sensing are now used to overcome the shortcomings of optical sensors. Images acquired by receiving the transmitted signals in radar sensors encounter speckle noise phenomena which is a granular noise. The noise exists inherently in SAR images due to interference of received signals either due to constructive or destructive addition of received signals that degrades the image quality and thus does not give accurate information. To denoise the images several techniques have been proposed and applied which includes local Filters, non-local filters and transforms. Each technique has its merits and demerits. Recent focus has been shifted towards the combined solution of non-local filters with transforms. The motivation of this study is to study the existing hybrid solution and work towards new venues in hybrid techniques to reduce information loss and produce better results.

Millimeter-Wave Ultrahigh Resolution SAR Image Classification Based on a New Feature Set


Aiming at the problems and prospects in millimeter-wave ultrahigh resolution synthetic aperture radar applications, we have developed a method with a new feature set for sophisticated classification of large images. It includes innovative parameters derived from different kinds of spectral and characteristic signatures, such as the correlation signature, radial spectrum, and angular spectrum. These features can mine repetitive information from the fragmented patterns and enhance the texture description in different aspects. In the experiment, the proposed feature set achieves 89% overall accuracy which is 25% higher compared with the gray-level co-occurrence matrix feature set. The four new features contribute to over 50% of the accuracy improvement with a significant increase of the accuracy for vehicles and show a fair performance for all the categories.

Towards photography through realistic fog


Imaging through fog has important applications in industries such as self-driving cars, augmented driving, airplanes, helicopters, drones and trains. Here we show that time profiles of light reflected from fog have a distribution (Gamma) that is different from light reflected from objects occluded by fog (Gaussian). This helps to distinguish between background photons reflected from the fog and signal photons reflected from the occluded object. Based on this observation, we recover reflectance and depth of a scene obstructed by dense, dynamic, and heterogeneous fog. For practical use cases, the imaging system is designed in optical reflection mode with minimal footprint and is based on LIDAR hardware. Specifically, we use a single photon avalanche diode (SPAD) camera that time-tags individual detected photons. A probabilistic computational framework is developed to estimate the fog properties from the measurement itself, without prior knowledge. Other solutions are based on radar that suffers from poor resolution (due to the long wavelength), or on time gating that suffers from low signal-to-noise ratio. The suggested technique is experimentally evaluated in a wide range of fog densities created in a fog chamber It demonstrates recovering objects 57cm away from the camera when the visibility is 37cm. In that case it recovers depth with a resolution of 5cm and scene reflectance with an improvement of 4dB in PSNR and 3.4× reconstruction quality in SSIM over time gating techniques.

Background Context-Aware-Based SAR Image Saliency Detection


Saliency, the distinctive parts of an image, has shown good potential for many applications (e.g., image interpretation and target detection). In this letter, a novel saliency detection method based on background context-aware is proposed for synthetic aperture radar (SAR) images. According to a statistical analysis of SAR image characteristics, several reference background patches (RBPs) are selected. Then, the dissimilarities between the current patch and the RBPs are used to calculate the local saliency, which is further enhanced for the final saliency under the multiscale framework. Experimental results demonstrate the effectiveness of the proposed method, which outperforms some state-of-the-art methods.

SAR image formation and exploitation using high resolution radar data


Synthetic Aperture Radar (SAR) images can be used in various applications such as Digital Elevation Model (DEM) generation, ground deformation monitoring, and detection of natural and man-made fine scale changes. After scanning the imaged scene, focusing algorithm is applied on raw radar data to obtain exploitable SAR images. With the availability of high resolution radar images, there is a real opportunity for studying the new applications of the SAR images. It is in this context that the present work was developed with a main objective of providing a further analysis of Range Doppler focusing algorithm. We are also interested by the use of the obtained SAR images in the application of change detection based on coherence estimation.

Automatic target recognition in SAR images: Comparison between pre-trained CNNs in a tranfer learning based approach


Synthetic aperture radar (SAR) are high resolution imaging radar systems. In many SAR applications classifying objects that are detected within the SAR image is important. In this paper an approach is proposed to tackle the Synthetic SAR Automatic Target Recognition (ATR) problem. The proposed scheme is based on a transfer leaning approach where three different pre-trained Convolutional Neural Networks (CNNs) are used as feature extractors in combination with a Support Vector Machine classifier (SVM). The CNNs used in this paper are AlexNet, VGG16 and GoogLeNet. The performance of these three CNNs is compared in regards to the SAR-ATR problem; where it is observed that AlexNet gives the best performance accuracy of 99.27%.