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Synthetic aperture radar (SAR) target detection plays a crucial role in military surveillance, earth observation, and disaster monitoring. With the development of deep learning (DL) and SAR imaging technology, numerous SAR target detection methods have been proposed and achieved better detection results. However, detecting different categories of SAR vehicle targets is still challenging due to the influence of coherent speckle noises and background clutter. This article presents a novel attention feature fusion awareness network (AFFNet) for vehicle target detection in SAR images. Specifically, we propose a multi-scale semantic attention (MSSA) module to obtain multi-scale and semantic features of target region; the variable multi-scale feature fusion (VMSFF) module is introduced to effectively fuse different feature information and alleviate target deformation interference by establishing feature correlation; the part feature awareness (PFA) module is used to obtain unique attribute of different vehicle targets to generate accurate anchor boxes. In addition, we design a candidate boundary box selection scheme, which can effectively adapt to SAR targets with different scales and categories. Overall, AFFNet is designed based on the SAR imaging mechanism and target physical feature information. To evaluate the performance of the proposed method, extensive experiments are conducted on the MSTAR dataset. The experiment results show that the proposed AFFNet obtains the mAP of 98.36 % and 97.26 % on standard operating conditions (SOCs) and extended operating conditions (EOCs), which is more efficient than the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the "beautiful countryside" construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide. [ABSTRACT FROM AUTHOR]

High dynamic range (HDR) images have recently drawn much attention in multimedia community. In this paper, we proposed an HDR image steganography method based on deep learning, which is for HDR images with OpenEXR format. To the best of our knowledge, this is the first steganography method that applies deep learning to HDR image steganography, and the first steganography method that hides images in HDR images. The LDR secret image is hidden in the mantissa of the HDR cover image of the same size through a hidden network, and recovered through an extraction network in the receiver. Experimental results show that the proposed algorithm has advantages in security, robustness and capacity compared with other “hiding image in image” algorithms. [ABSTRACT FROM AUTHOR]

Profiting from the powerful feature extraction and representation capabilities of deep learning (DL), aerial image semantic segmentation based on deep neural networks (DNNs) has achieved remarkable success in recent years. Nevertheless, the security and robustness of DNNs deserve attention when dealing with safety-critical earth observation tasks. As a typical attack pattern in adversarial machine learning (AML), backdoor attacks intend to embed hidden triggers in DNNs by poisoning training data. The attacked DNNs behave normally on benign samples, but when the hidden trigger is activated, its prediction is modified to a specified target label. In this article, we systematically assess the threat of backdoor attacks to aerial image semantic segmentation tasks. To defend against backdoor attacks and maintain better semantic segmentation accuracy, we construct a novel robust generative adversarial network (RFGAN). Motivated by the sensitivity of human visual systems to global and edge information in images, RFGAN designs the robust global feature extractor (RobGF) and the robust edge feature extractor (RobEF) that force DNNs to learn global and edge features. Then, RFGAN uses robust global and edge features as guidance to obtain benign samples by the constructed generator, and the discriminator to obtain semantic segmentation results. Our method is the first attempt to address the backdoor threat to aerial image semantic segmentation by constructing the robust DNNs model architecture. Extensive experiments on real-world scenes aerial image benchmark datasets demonstrate that the constructed RFGAN can effectively defend against backdoor attacks and achieve better semantic segmentation results compared with the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety-critical systems. Existing research mainly focuses on solving digital attacks for aerial image semantic segmentation, but adversarial patches with physical attack attributes are more threatening than digital attacks. In this article, we systematically evaluate the threat of adversarial patches on the aerial image semantic segmentation task for the first time. To defend against adversarial patch attacks and obtain accurate semantic segmentation results, we construct a novel robust feature extraction network (RFENet). Based on the characteristics of aerial images and adversarial patches, RFENet designs a limited receptive field mechanism (LRFM), a spatial semantic enhancement module (SSEM), a boundary feature perception module (BFPM) and a global correlation encoder module (GCEM), respectively, to solve adversarial patch attacks from the DL model architecture design level. We discover that semantic features, shape features and global features contained in aerial images can significantly enhance the robustness of the DL model against patch attacks. Extensive experiments on three aerial image benchmark datasets demonstrate that the proposed RFENet has strong resistance to adversarial patch attacks compared with the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The efficient and automatic detection of chest abnormalities is vital for the auxiliary diagnosis of medical images. Many studies utilize computer vision and deep learning approaches involving symmetry and asymmetry concepts to detect chest abnormalities, and achieve promising findings. However, an accurate instance-level and multi-label detection of abnormalities in chest X-rays remains a significant challenge. Here, a novel anomaly detection method for symmetric chest X-rays using dual-attention and multi-scale feature fusion is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We improved the deep neural network with channel-dimensional and spatial-dimensional attention to capture the abundant contextual features. We then used an optimized multi-scale learning framework for feature fusion to adapt to the scale variation in the abnormalities. Considering the influence of the data imbalance and other factors, we introduced a seesaw loss function to flexibly adjust the sample weights and enhance the model learning efficiency. The rigorous experimental evaluation of a public chest X-ray dataset with fourteen different types of abnormalities demonstrates that our model has a mean average precision of 0.362 and outperforms existing methods. [ABSTRACT FROM AUTHOR]

Building contour extraction from high-resolution remote sensing images is a basic task for the reasonable planning of regional construction. Recently, building segmentation methods based on the U-Net network have become popular as they largely improve the segmentation accuracy by applying 'skip connection' to combine high-level and low-level feature information more effectively. Meanwhile, researchers have demonstrated that introducing an attention mechanism into U-Net can enhance local feature expression and improve the performance of building extraction in remote sensing images. In this paper, we intend to explore the effectiveness of the primeval attention gate module and propose the novel Attention Gate Module (AG) based on adjusting the position of 'Resampler' in an attention gate to Sigmoid function for a building extraction task, and a novel Attention Gates U network (AGs-Unet) is further proposed based on AG, which can automatically learn different forms of building structures in high-resolution remote sensing images and realize efficient extraction of building contour. AGs-Unet integrates attention gates with a single U-Net network, in which a series of attention gate modules are added into the 'skip connection' for suppressing the irrelevant and noisy feature responses in the input image to highlight the dominant features of the buildings in the image. AGs-Unet improves the feature selection of the attention map to enhance the ability of feature learning, as well as paying attention to the feature information of small-scale buildings. We conducted the experiments on the WHU building dataset and the INRIA Aerial Image Labeling dataset, in which the proposed AGs-Unet model is compared with several classic models (such as FCN8s, SegNet, U-Net, and DANet) and two state-of-the-art models (such as PISANet, and ARC-Net). The extraction accuracy of each model is evaluated by using three evaluation indexes, namely, overall accuracy, precision, and intersection over union. Experimental results show that the proposed AGs-Unet model can improve the quality of building extraction from high-resolution remote sensing images effectively in terms of prediction performance and result accuracy. [ABSTRACT FROM AUTHOR]