Target detection for synthetic aperture radar (SAR) images has great influence on the successive discrimination based on the target regions. However, as a pixel-based method, the traditional constant false alarm rate (CFAR) detection could not work well for the ship target detection problem of multiple ship targets with different sizes in a SAR image, which is referred to as the multiscale situation. Moreover, it needs to use the clustering method on the pixel-level detection results to obtain the accurate target regions, which may merge two or more different targets into a target region. In this letter, a modified CFAR based on object proposals is proposed. We use the object proposal generator to generate a small set of object proposals with different sizes, and then use the proposal-based CFAR detector, where the extracted object proposals are regarded as the guard windows instead of setting fixed guard window, to detect the true positive object proposals. By introducing the object proposals as the variable guard windows in the CFAR detector, the proposed algorithm could gain good detection performance in the multiscale situation, since the missed detection resulting from the big differences between the sizes of the fixed guard window and ship targets can be avoided. Meanwhile, the proposed method can directly obtain the accurate target regions. The effectiveness of the proposed algorithm is verified using the measured SAR data.

A Modified CFAR Algorithm Based on Object Proposals for Ship Target Detection in SAR Images

Convolutional neural networks (CNNs) have found applications in ship detection from synthetic aperture radar (SAR) images. However, there are some challenges hamper their advance. First, the detected bounding boxes are not very compact. Second, there are quite a few missing detections for small and densely clustered ships. Third, objects with analogical scatterings on land are detected as ships by making mistake. This is due to: 1) the CNN-based SAR ship detectors cannot utilize the spatial information very sufficiently; 2) features learned from CNNs only describe SAR images in space domain while neglecting the information hidden in frequency domain; and 3) information contained in the meta-data file, which may link to other sources, is not taken into account. To overcome these problems, in this paper, a cascade coupled CNN-guided (3C2N-guided) visual attention method for SAR ship detection is proposed. This method considers the newly presented 3C2N model as a qualified ship proposal generator because the images’ spatial information is utilized more sufficiently. The 3C2N model, with coupled CNN as the baseline, consists of a sequence of cascade detectors for training. Complementally, a pulse cosine transformation-based visual attention model in frequency domain is operated on the adaptive regions for ship discrimination. This could further refine the proposals’ locations and could significantly reduce the missing detections and false alarms. In addition, the digital elevation model data are adopted to remove ship-like targets on land. Experimental evaluations on 25 Sentinel-1 images demonstrate that the proposed method is superior to the previous state-of-the-art methods.

A Cascade Coupled Convolutional Neural Network Guided Visual Attention Method for Ship Detection From SAR Images

Since only the spatial feature information of ship target is utilized, the current deep learning-based synthetic aperture radar (SAR) ship detection approaches cannot achieve a satisfactory performance, especially in the case of multiscale or rotations, and the complex background. To overcome these issues, a novel multidimensional domain deep learning network for SAR ship detection is developed in this work to exploit the spatial and frequency-domain complementary features. The proposed method consists of the following main three steps. First, to learn hierarchical spatial features, the feature pyramid network (FPN) is adopted to produce ship target spatial multiscale characteristics with a top-down structure. Second, with a polar Fourier transform, the rotation-invariant features of SAR ship targets are obtained in the frequency domain. After that, a novel spatial-frequency characteristics fusion network is then presented, which seeks to learn more compact feature representations across different domains by updating the parameters of sub-networks interactively. The detection results are obtained due to utilizing the multidimensional domain information, and we evaluate the effectiveness of the proposed method using the existing SAR ship detection data set (SSDD). The results of the proposed method outperform other convolutional neural network (CNN)-based algorithms, especially for multiscale and rotation ship targets under complex backgrounds. 

A Novel Multidimensional Domain Deep Learning Network for SAR Ship Detection

Permafrost is widely distributed in the Tibetan Plateau. Seasonal freeze–thaw cycles of permafrost result in upward and downward surface displacement. Multitemporal interferometric synthetic aperture radar (MT-InSAR) observations provide an effective method for monitoring permafrost displacement under difficult terrain and climatic conditions. In this study, a seasonal sinusoidal model-based new small baselines subset (NSBAS) chain was adopted to obtain a deformation time series. An experimental study was carried out using 33 scenes of Sentinel-1 data (S-1) from 28 November 2017 to 29 December 2018 with frequent revisit (12 days) observations. The spatial and temporal characteristics of the surface displacements variation combined with different types of surface land cover, elevation and surface temperature factors were analyzed. The results revealed that the seasonal changes observed in the time series of ground movements, induced by freeze–thaw cycles were observed on flat surfaces of sedimentary basins and mountainous areas with gentle slopes. The estimated seasonal oscillations ranged from 2 mm to 30 mm, which were smaller in Alpine deserts than in Alpine meadows. In particular, there were significant systematic differences in seasonal surface deformation between areas near mountains and sedimentary basins. It was also found that the time series of deformation was consistent with the variation of surface temperature. Based on soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data, the deformation analysis influenced by soil moisture factors was also carried out. The comprehensive analysis of deformation results and auxiliary data (elevation, soil moisture and surface temperature et al.) provides important insights for the monitoring of the seasonal freeze-thaw cycles in the Tibetan Plateau.

https://www.mdpi.com/2072-4292/11/9/1000/pdf

Time-Series InSAR Monitoring of Permafrost Freeze-Thaw Seasonal Displacement over Qinghai-Tibetan Plateau Using Sentinel-1 Data

Remote sensing of photovoltaic scenarios: Techniques, applications and future directions

Ship detection can be significantly improved by using polarimetric synthetic aperture radar (PolSAR) imaging. In this article, we propose a PolSAR ship detection method based on the use of multi-featured polarization by using the visual attention model. Three polarimetric features, namely, the polarimetric contrast, the polarimetric scattering, and the polarimetric phase, are selected as the early features, and the pros and cons for each feature are discussed. The visual attention model is a framework that rapidly combines multiple features into one feature, which is improved according to the relationship of the selected features. Validation of the method is performed by analysing the multi-resolution process, the improved multi-feature process, the threshold strategy, the sensibility to the incidence angle of the sensors, and the performance of moving ship detection, which are analysed by Radarsat-2 fine quad images with automatic identification system data. Additionally, the false alarm/non-detection an...

A PolSAR ship detector based on a multi-polarimetric-feature combination using visual attention

Accurate estimation of tropospheric delay is significant for global navigation satellite system’s (GNSS) high-precision navigation and positioning. However, due to the random and contingent changes in weather conditions and water vapor factors, the classical tropospheric delay model cannot accurately reflect changes in tropospheric delay. In recent years, with the development of meteorological observation/detection and numerical weather prediction (NWP) technology, the accuracy and resolution of meteorological reanalysis data have been effectively improved, providing a new solution for the inversion and modeling of regional or global tropospheric delays. Here, we evaluate the consistency and accuracy of three different types of reanalysis data (i.e., ERA5, MERRA2, and CRA40) used to invert the zenith tropospheric delay (ZTD) from 436 international GNSS service (IGS) stations in 2020, based on the integral method. The results show that the ZTD inversion of the three types of reanalysis data was consistent with the IGS ZTD, even in heavy rain conditions. Furthermore, the average precision of the ZTD inversion of the ERA5 reanalysis data was higher, where the mean deviation (bias), mean absolute error (MAE), and root mean square (RMS) were –3.39, 9.69, and 12.55 mm, respectively. The ZTD average precisions of the MERRA2 and CRA40 inversions were comparable, showing slightly worse performance than the ERA5. In addition, we further analyzed the global distribution characteristics of the ZTD errors inverted from the reanalysis data. The results show that ZTD errors inverted from the reanalysis data were highly correlated with station latitude and climate type, and they were mainly concentrated in the tropical climate zone at low latitudes. Compared to dividing error areas by latitude, dividing error areas by climatic category could better reflect the global distribution of errors and would also provide a data reference for the establishment of tropospheric delay models considering climate type.

/pdf/accuracy-evaluation-and-analysis-of-gnss-tropospheric-delay-2b6wz7a9.pdf

Accuracy Evaluation and Analysis of GNSS Tropospheric Delay Inversion from Meteorological Reanalysis Data

Using synthetic aperture radar (SAR) for ship detection is a significant application for maritime monitoring and security. Ship detectors depress the disturbance from the sea surface such that the background consistently shows the sea pixels. However, in addition to water, the vast sea also contains other real objects, of which small islands are the most common. In this paper, we investigated the detection of ships in a sea area containing small islands. Based on the most widely used ship detector-constant false alarm rate (CFAR), three aspects were selected to investigate the island appearance effect, namely, image resolution, polarization, and incidence angle. As the result, an output strategy is provided when an island exists, and a ship-island enhance operator is proposed.

SAR-based ship detection in sea areas containing small islands

Object detection from remote sensing images is an interesting research topic full of theoretical and application values. Compared with generic images, those of remote sensing contain much less distinguishing representation information due to far-away capturing position and limited camera lens resolution, the perpendicular to ground capturing view, almost unacquirable 3-D object structure and serious roof occlusion, and the inherent unimportance of top roof view. Focusing on this intrinsic disadvantage, in this letter, we propose a general framework of accurate remote sensing object localization constructed with the core idea of maximum image feature exploration. This integrated structure is mainly comprised of cascaded tiny patch correlation (CTPC) based feature digging, averaging local patch regression (ALPR) centered coarse location acquisition, and instance segmentation oriented further refinement (ISOFR). All those components are designed to maximize feature digging and usage. We believe this is the first to implement the idea of maximum feature exploration to each corner of object detection architecture. Extensive experiments on challenging aerial image datasets DOTA and NWPU VHR-10 show state-of-the-art performance.

A Framework of Maximum Feature Exploration Oriented Remote Sensing Object Detection

Ship detection is a significant application of maritime monitoring and security To fully explore the potential of wide
coverage of synthetic aperture radar (SAR) image, the ScanSAR Wide image for ship detection is investigated in this
paper The Radarsat-2 ScanSAR Wide mode image is used as the image source due to its huge coverage and constant
false alarm rate (CFAR) with Gamma distribution is selected as the core detector Two problems of ScanSAR ship
detection, the unbalanced phenomenon and false alarms of islands, are investigated and solved by a compensation step
and Hessian matrix respectively For more aspects, the detector also concerns the polarization channel selection and
distribution fitting Finally, a whole flow chart of ScanSAR ship detection is presented As test cases, the experimental
image is used to show the efficiency of our method

Ship surveillance with Radarsat-2 ScanSAR

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