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

Land contained sea area ship detection using spaceborne image

Synthetic aperture radar (SAR) ship detection is of significant importance in military and commercial applications. However, a high similarity in intensity and spatial distribution of scattering characteristics between the ship target and harbor facilities, along with a fuzzy sea-land boundary due to the strong speckle noise, result in a low detection accuracy and high false alarm rate for SAR ship detection with complex inshore scenes. In this paper, a new inshore ship detection method based on multi-modality saliency is proposed to overcome these challenges. Four saliency maps are established from different perspectives: an ocean-buffer saliency map (OBSM) outlining more accurate coastline under speckle noises; a local stability saliency map (LSSM) addressing pixel spatial distribution; a super-pixel saliency map (SPSM) extracting critical region-based features for inshore ship detection; and an intensity saliency map (ISM) to highlight target pixels with intensity distribution. By combining these saliency maps, ship targets in complex inshore scenes can be successfully detected. The method provides a novel interdisciplinary perspective (surface metrology) for SAR image segmentation, discovers the difference in spatial characteristics of SAR image elements, and proposes a novel robust CFAR procedure for background clutter fitting. Experiments on a public SAR ship detection dataset (SSDD) shows that our method achieves excellent detection performance, with a low false alarm rate, in offshore scenes, inshore scenes, inshore scenes with confusing metallic port facilities, and large-scale scenes. The results outperform several widely used methods, such as CFAR-based methods and super-pixel methods. 

Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images

Due to the lack of color, the strong speckle noise, and the complex background clutter, target detection in synthetic aperture radar (SAR) images is a challengeable task. A novel saliency and background prior (SBP)-based residential area detection method for SAR images is proposed in this letter. It has three major advantages compared with other methods: 1) in saliency analysis, it deeply exploits the image feature and conceives a new texture representation using the amplitude of partitioning Fourier transform (pFT), which compensates for the lack of color and spectrum information in SAR; 2) it employs the superpixel-level background prior and monitors the average intensity level (AIL) of each superpixel for generating accurate outlines of residential areas; and 3) two regional feature-based indices are presented to select the background clutter, and the results serve as a modification to saliency analysis. Experiments using ALOS PALSAR images show that the proposed method has great priority in both quality and quantity over competing methods by extracting integrated residential areas with clear boundaries.

Saliency and Background Prior-Based Residential Area Detection for SAR Images

The rapid development of the satellite observation technology provides a very rich source of data for sea reconnaissance and ships surveillance. In the face of such a vast sea remote sensing data, it is urgent need to realize the automatic ship detection in optical remote sensing images, but the optical remote sensing images are easily affected by meteorological conditions, such as clouds, waves, which results in larger false alarm; and the weak contrast between optical remote sensing image target and background is easy to cause missing alarm. In this paper, a novel algorithm based on wavelet transform for ship target detection in optical remote sensing images is proposed, which can effectively remove these noise and interference. The segmentation of sea and land background is first applied to the image preprocessing to achieve more accurate detection results, and then discrete wavelet transform is used to deal with the part of sea background. The results show that almost all of the offshore ships can be detected, and through the comparison of the results of four different wavelet basis functions, the accuracy of ship detection is further improved.

A novel algorithm based on wavelet transform for ship target detection in optical remote sensing images

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

Automatic extraction of residential area from SAR image is a difficult task due to its complexity. The traditional methods based on segmentation or classification are effective. In this article, we present a novel method based on conditional generative adversarial networks (CGANs) to extract regular residential area in rural and urban region. CGANs is applied to extend the scope of research from supervised learning to semi supervised learning and adversarial training is used to improve the training effect. The experimental results show that the proposed method can achieve better results than traditional methods.

Residential area extraction based on conditional generative adversarial networks

Digital elevation model (DEM) matching is a necessary task that must be conducted in many applications of remote sensing, especially for purposes of disaster surveillance (e.g., landslide, debris flow). In this paper, we propose a robust matching method based on contour lines and least squares surface matching. Challenges involve detecting stable and invariant control points (CPs) in un-deformed areas in varying situations and complex topographies. We utilize a shape context descriptor to compare contour lines and detect invariant peaks for CPs based on contour line shapes, which can generate good initial values of transformation parameters. Finally, the least squares surface matching technique is used for optimal matching. The proposed methodology is tested for DEM matching between a new InSAR DEM and ASTER DEM. The experimental results show that the proposed method shows considerable noise resistance and can alleviate deformation and nonlinear distortion effects of different systems.

A new method for multisource DEM matching

Owing to the effect of imaging mechanism and imaging conditions in synthetic aperture radar (SAR) image, inconsistent features and relationship correspondence constitute key problems using traditional image matching algorithms because of significant differences between the images. This study proposes an object-based SAR image matching method. Two images are matched through same ground objects, by means of property and shape information of objects, which are obtained via object extraction and morphological operations. We utilize a shape context descriptor to compare contours of objects and detected invariant control points. The experimental results show that the proposed method achieves reliable and stable matching performance, and can alleviate deformation and nonlinear distortion effects of different systems.

/pdf/object-based-multi-mode-sar-image-matching-2l0j7c45f0.pdf

Object-Based Multi-mode SAR Image Matching

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SAR-based ship detection in sea areas containing small islands

Residential area extraction based on conditional generative adversarial networks

A new method for multisource DEM matching

Object-Based Multi-mode SAR Image Matching