A Novel Imaging Algorithm for High-Resolution Wide-Swath Space-Borne SAR Based on a Spatial-Variant Equivalent Squint Range Model

High-Resolution Wide-Swath (HRWS) is an important development direction of space-borne Synthetic Aperture Radar (SAR). The two-dimensional spatial variation of the Doppler parameters is the most significant characteristic of the sliding spotlight space-borne SAR system under the requirements of HRWS. Therefore, the compensation of the two-dimensional spatial variation is the most challenging problem faced in the imaging of HRWS situations. The compensatory approach is then proposed to address this problem in this paper. The spatial distribution of the Doppler parameters for the HRWS space-borne SAR data in the sliding spotlight working mode is firstly analyzed, based on which a Spatial-Variant Equivalent Slant Range Model (SV-ESRM) is put forward to accurately formulate the range history for the distributed target. By introducing an azimuth-varying term, the SV-ESRM can precisely describe the range history for not only central targets but also marginal targets, which is more adaptive to the HRWS space-borne SAR requirements. Based on the SV-ESRM, a Modified Hybrid Correlation Algorithm (MHCA) for HRWS space-borne SAR imaging is derived to focus the full-scene data on one single imaging processing. A Doppler phase perturbation incorporated with the sub-aperture method is firstly performed to eliminate the azimuth variation of the Doppler parameters and remove the Doppler spectrum aliasing. Then, an advanced hybrid correlation is employed to achieve the precise differential Range Cell Migration (RCM) correction and Doppler phase compensation. A range phase perturbation method is also utilized to eliminate the range profile defocusing caused by range-azimuth coupling for marginal targets. Finally, a de-rotation processing is performed to remove the azimuth aliasing and the residual azimuth-variance and obtain the precisely focused SAR image. Simulation shows that the SAR echoes for a 20 km × 20 km scene with a 0.25 m resolution in both the range and azimuth directions could be focused precisely via one single imaging processing, which validates the feasibility of the proposed algorithm.

Estimation of the Conditional Probability Using a Stochastic Gradient Process

The use of conditional probabilities has gained in popularity in various fields such as medicine, finance, and imaging processing. This has occurred especially with the availability of large datasets that allow us to extract the full potential of the available estimation algorithms. Nevertheless, such a large volume of data is often accompanied by a significant need for computational capacity as well as a consequent compilation time. In this article, we propose a low-cost estimation method: we first demonstrate analytically the convergence of our method to the desired probability and then we perform a simulation to support our point.

Radar and Sonar Imaging and Processing (2nd Edition)

The 14 papers (from 29 submitted) published in the Special Issue “Radar and Sonar Imaging Processing (2nd Edition)” highlight a variety of topics related to remote sensing with radar and sonar sensors. The sequence of articles included in the SI deal with a broad profile of aspects of the use of radar and sonar images in line with the latest scientific trends, in which the latest developments in science, including artificial intelligence, were used.

Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images

Change detection for remote sensing images is an indispensable procedure for many remote sensing applications, such as geological disaster assessment, environmental monitoring, and urban development monitoring. Through this technique, the difference in certain areas after some emergencies can be determined to estimate their influence. Additionally, by analyzing the sequential difference maps, the change tendency can be found to help to predict future changes, such as urban development and environmental pollution. The complex variety of changes and interferential changes caused by imaging processing, such as season, weather and sensors, are critical factors that affect the effectiveness of change detection methods. Recently, there have been many research achievements surrounding this topic, but a perfect solution to all the problems in change detection has not yet been achieved. In this paper, we mainly focus on reducing the influence of imaging processing through the deep neural network technique with limited labeled samples. The attention-guided Siamese fusion network is constructed based on one basic Siamese network for change detection. In contrast to common processing, besides high-level feature fusion, feature fusion is operated during the whole feature extraction process by using an attention information fusion module. This module can not only realize the information fusion of two feature extraction network branches, but also guide the feature learning network to focus on feature channels with high importance. Finally, extensive experiments were performed on three public datasets, which could verify the significance of information fusion and the guidance of the attention mechanism during feature learning in comparison with related methods.

Computational Imaging-Processing Comparison for Lumbar Spine Cadaveric Specimens with Clinical Medical Physics Applications

A series of improved imaging-computational and algorithmic methods for new/different lumbar cadaveric specimens was obtained. These are based on previous publications [3,3.1], with an improved-imaging research line. Results show a systematic study of each lumbar cadaveric specimen. Enhanced imaging findings and resolution for vertebral facets/positioning, contrast, anatomical parts separation and visualization of lumbar spines are demonstrated. Medical Physics and clinical bioengineering advances related to previous contributions are proven with imaging processing, programming codes/patterns, and computer vision tools. Findings constitute computational imaging methods which are appropriate for sharp and detailed anatomical-clinical analysis and comparisons among cadaveric specimens. These processing solutions are useful for lumbar spine computational study and anatomical dissection. Applications on Medical Physics, Biomedical Engineering, and Computational-Forensic Diagnosis are obtained from this cadaveric imaging systematic comparison and software methods.

A 3-D SAR Imaging Method Based on Back Projection Algorithm

3D back projection (BP) algorithm is an imaging algorithm based on time domain echo data, which effectively solves the overlapping mask problem existing in 2D SAR. It can complete the imaging processing of echo signal under any geometry configuration, and has the advantages of high target focusing accuracy and high phase preservation. However, the high complexity and low efficiency of 3D BP imaging algorithm limit its application and development. In this paper, a 3d imaging method based on improved back projection algorithm is proposed. Aiming at the problem that existing imaging algorithms need 2D imaging first and then 3D imaging, an improved 3D BP algorithm is proposed to directly 3D imaging, which avoids 2d imaging processing. The proposed method simplifies the steps of the traditional 3D BP algorithm and improves the efficiency of the algorithm. The validity and effectiveness of the proposed method are verified by the 3d imaging results of simulated lattice targets.

Wide-field photothermal reflectance spectroscopy for single nanoparticle absorption spectrum analysis

Photothermal imaging is useful for detecting individual nanoparticles and obtaining the absorption spectra. This study presents a wide-field photothermal reflectance spectroscopy technique achieved by incorporating a pump beam, a probe beam, and a charge-coupled device (CCD) camera into a commercial microscopic setup. The presented design does not require precise alignment between the pump and the probe beams and enables the observation of numerous individual nanoparticles during image acquisition. Despite the use of a simple imaging processing method, i.e., a four-bucket method using a CCD camera, sufficient sensitivity for the spectral imaging of a single gold nanorod (20 nm diameter and 84 nm length) is demonstrated. Numerous individual nanoparticles within a wide field of view (240 μm × 180 μm) are detected in an image captures at an imaging measurement speed of 0.02 mm2 min−1. Furthermore, the proposed photothermal reflectance spectroscopy technique can detect the variation in the absorption peak of the measured spectra depending on the aspect ratio of individual nanoparticles within a spectral resolution of 1 nm.

Идентификация пор ультрафильтрационных мембран на основе электронно-микроскопических исследований

В работе проанализированы методы, способы, приемы и прикладные программы для идентификации пор в полимерных мембранах. На основе сравнения достоинств и недостатков методов предложен подход к разработке программной реализации исследования пор полимерных полупроницаемых мембран. Объектами исследования служили ультрафильтрационные мембраны вида УАМ-50, УАМ-100, УПМ-К, УПМ-100, выбор которых обеспечен высокой задерживающей способностью, хорошей производительностью и наибольшей применяемостью в промышленной практике. Приведена методика по расчету коэффициента засоренности мембран, которая позволяет определить срок эффективной работы ультрафильтрационных мембран, элементов и установок при баромембранном и электробаромембранном разделении, концентрировании и очистке промышленных растворов и стоков. Выделенные участки ультрафильтрационных мембран УАМ-50, УАМ-100, УПМ-К, УПМ-100 площадью 1000000 нм2 обрабатывались при помощи Matlab 2017 таким образом, что были получены основные параметры, такие как средний диаметр засоренности (диаметры пор и коэффициент засоренности мембран). При обработке больших массивов данных по средним диаметрам пор и коэффициенту засоренности мембран использовался ПК, который позволил снизить и рассчитать погрешность выполненных измерений при помощи стандартных методов математической статистики. Расчет коэффициента засоренности мембран производили при помощи программы, изучающей описание основных функций imaging processing toolbox. Разработанный метод существенно сокращает время эксперимента и позволяет автоматизировано рассчитывать количество объектов, среднюю площадь, диаметр пор на сорбционной поверхности. Метод, сочетающий электронно-микроскопические исследования, обработку изображений Otsu's method, программную реализацию в Matlab 2017, дают возможность получить достоверные и воспроизводимые данные по морфологии поверхности ультрафильтрационных мембран УАМ-50, УАМ-100, УПМ-К, УПМ-100, опирающихся на статистическую обработку большой выборки данных, полученных в результате электронно-микроскопических исследований. Анализ экспериментальных данных, полученных автоматизированным методом, показал, что средняя площадь объекта наименьшая для мембраны УПМ-К и наибольшая для мембраны УАМ-50, а средний диаметр пор поверхности исследуемых мембран находится в интервале от 51 до 60 нм, что сопоставимо с литературными данными, полученных другими методами. При этом коэффициент засоренности выше для мембраны УПМ-К и ниже для мембраны УПМ-100.