Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends

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

Optical and synthetic aperture radar (SAR) images provide various complementary information on land properties, which can substantially improve the accuracy of land cover classification because of the fusion of their multisource information. However, excellent extraction of the discriminative information of optical and SAR images and effective integration of the interpretation information of multisource data remain challenging issues. In this study, we have proposed a novel joint network that combines a dual-attention fusion modality and two specific modalities to achieve superior land cover classification maps, which has two modes of encoding-level fusion (JoiTriNet-e) and decoding-level fusion (JoiTriNet-d). We first proposed a strategy for the fusion modality and specific modalities joint learning, the goal of which was to simultaneously find three mapping functions that project optical and SAR images separately and together into semantic maps. Two architectures were constructed using the proposed strategy, which improved the performance of multisource and single-source land cover classification. To aggregate the heterogeneous features of optical and SAR images more reasonably, we designed a multimodal dual-attention fusion module. Experiments were conducted on two multisource land cover datasets, among which comparison experiments highlighted the superiority and robustness of our model, and ablation experiments verified the effectiveness of the proposed architecture and module.

Joint Network Combining Dual-Attention Fusion Modality and Two Specific Modalities for Land Cover Classification Using Optical and SAR Images

Owing to significant differences in synthetic aperture radar (SAR) images caused by diverse imaging mechanisms and imaging conditions, inconsistent features and relationship correspondences constitute key problems for traditional image registration algorithms. This study presents a novel SAR image registration method based on the shape information of distinct ground objects, which is obtained via object extraction and morphological operations. We utilize a shape context descriptor to compare the contours of objects and detect invariant control points. The experimental results show that the proposed method can achieve a reliable and stable registration performance for SAR images of different sensors.

Multi-Sensor SAR Image Registration Based on Object Shape

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

In high-resolution remote sensing images, road scale diversity and occlusions caused by shadows, buildings, and vegetation often pose challenges for road extraction. Currently, end-to-end models constructed using deep convolutional neural networks are widely used in road extraction and have significantly improved the accuracy of this task. However, the connectivity and completeness of their results require improvement. This article proposes a dual task-driven deep convolutional neural network constructed by combining road shape patterns and scale differences. The mainline task is road-surface segmentation, the encoder of which employs residual convolution for feature extraction. The decoder comprises a multiscale and multidirection strip convolution module, the output of which is the final extraction result. The splitting task is road centerline extraction, the input features of which come from the coding layer of the road-surface segmentation branches. The intermediate features are incorporated into the decoder of the road-surface segmentation branches, to fully exploit the road centerline and thus improve the road-surface segmentation result connectivity. Implementation of the proposed method on the CHN6-CUG and DeepGlobe datasets reveals superior performance to comparative methods as regards quantitative evaluation metrics; evident advantages for road coverings, road intersections, and low-scale roads; greater model portability; and better small-sample learning capability.

https://ieeexplore.ieee.org/ielx7/4609443/4609444/10162179.pdf

Dual-Task Network for Road Extraction From High-Resolution Remote Sensing Images

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

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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