Remote-sensing scene classification (RSSC) is crucial for remote-sensing image interpretation and has become a research hotspot in recent years. However, the high complexity of remote-sensing scenes causes most RSSC models to fail to accurately capture key objects, resulting in low classification accuracy. Meanwhile, it is intractable to effectively distinguish similar scenes, such as forest and meadow, whose semantic labels are mainly determined by wide-scale features. In addition, existing remote-sensing attention mechanisms are heuristic settings, which require expert knowledge and extensive experiments. To solve the above problems, a novel plug-and-play hyperparameter-free attention module (HFAM) based on feature map mathematical calculation is proposed in this work. HFAM uses statistical indicators to quantitatively characterize the fluctuations of feature maps that can accurately locate key features and distinguish different scenes, alleviating the problems of intraclass diversity and interclass similarity. Moreover, HFAM adaptively acquires attention weights by performing simple mathematical calculations on the feature maps, which solves the problem of difficult adjustment of hyperparameters. Our proposed HFAM can be expediently inserted into the existing ConvNet models without increasing the number of model’s parameters. Extensive contrast experiments with several famous plug-and-play attention modules on three mainstream datasets reveal the superiority of our HFAM in accuracy, number of parameters, and calculation amount. Moreover, compared with state-of-the-art methods, it also demonstrated considerable competitiveness.

Oriented object detectors have achieved great success in aerial detection tasks with the help of ample labeled data. Unlabeled images are easier and less expensive to obtain than labeled aerial images. Therefore, semi-supervised oriented object detection (SSOOD) is becoming a hot task, which can leverage both labeled and unlabeled data to train oriented detectors. Most SSOOD approaches focus on well-designed approaches to generate high-quality pseudo labels (PLs) or positive learning regions, which are limited to complex and variable aerial scenes. This study first analyzes key factors influencing the performance of SSOOD and proposes a global focal learning method (termed as focal teacher) without artificial priori design. It relies on global region and soft regression approaches to blur the boundaries between positive and negative samples, mainly through localization focal loss to achieve. It leverages the localization consistency between the teacher and student model to focus more on hard regions. Moreover, we organize a large remote sensing unlabeled (RSUL) dataset to exploit the performance potential of oriented detectors on mainstream aerial detection datasets (DOTA and DIOR). Adequate experiments reveal that the proposed method achieves the best performance compared with other mainstream SSOOD methods, including partly, fully, and additional data settings on DOTA and DIOR datasets. Semi-supervised mechanisms without preset learning regions can be better applied in dense and complex aerial scenes.

Rotation object detection is of great importance in remote sensing imagery where the orientation is arbitrary and objects are densely distributed. However, there are several challenges that need to be overcome, such as the angular boundary problem in the regression-based methods and the square-like problem in the classification-based methods. For square-like object rotation detection, classification-based methods [e.g., circular smooth label (CSL)] suffer from inconsistencies between angular coding and evaluation mechanisms due to the variation of aspect ratio. To address the angular inconsistencies of square-like object existing in current classification methods, we design a novel angular encoding mechanism based on aspect ratio. We make optimizations and improvements in the following two aspects: 1) proposing an aspect ratio-based bidirectional coded label (AR-BCL) to replace CSL for angle coding of square-like object, which significantly improves detection accuracy for square-like object and ii) designing a cross-fusion decoupled head (CF-DH) based on angular classification to replace the existing coupled head (CH), which can help extract features that suitable for angular classification. Extensive experiments on DOTA, a large-scale public dataset for aerial images, demonstrate the effectiveness of our method for square-like object detection.

Oriented object detection plays an important role in aerial image interpretation. Image processing speed is also essential due to massive amounts of aerial images. Anchor-free oriented detectors with fast processing speed are generally accepted despite the absence of pre-set anchors, contributing to their performance gap with anchor-based detectors. Most anchor-free oriented detectors are carefully designed by defining samples according to target characteristics, which require substantial prior knowledge, to realize improved performance. This study proposes an anchor-free oriented detector (termed as rfpoint) that requires minimal prior knowledge. Moreover, this study mainly aims to introduce a dynamic sample definition strategy. This strategy is modeled as a dynamic regulating process, wherein the classification and box regression interact until the model converges. A rotating quality-driven loss (RQDL) and adaptive-weight box loss (AWBL) are also proposed to realize the aforementioned process. RQDL redefines positive and negative attributes of samples according to the distribution of rotating Intersection-over-Unit (IoU) between predictions and ground truth. AWBL adjusts the importance degree of candidate samples in the box regression through classification scores. The proposed method is then tested on three mainstream aerial image datasets (DOTA, DIOR, and HRSC2016). Results reveal that the proposed method achieves the best performance compared with other oriented detectors, whose mAP are 79.92%, 70.88%, and 90.67%. Moreover, the method maintains the inference speed advantage of anchor-free detectors. An effective sample definition method can bridge the performance gap of anchor-free oriented detectors without minimizing inference speed.

Road segmentation in remote sensing images has been widely used in many fields. Semantic segmentation, based on deep learning, has become a hot topic for road segmentation. With the deepening of convolutional neural network (CNN) structures, features in the convolution layer that has more semantic information become more important for road segmentation. However, the spatial resolution of the convolutional layer reduced as the CNN network deepens, which causes the extracted roads to lose some important location information. To solve this problem, this letter proposes a novel end-to-end road segmentation method to effectively utilize the different levels of convolutional layers to enhance the model’s ability to precisely perceive road edges and shapes. The model includes an encoder and a decoder. The encoder encodes the image to obtain the features of different levels and scales. The decoder consists of two modules: scale fusion module and scale sensitive module. In the scale fusion module, features in pooling layers of different scales are fused to obtain a fusion feature. In a scale sensitive module, a weight tensor at the end of the network is learned to evaluate the importance of fusion features. This road segmentation network has been experimentally verified using public data sets, which greatly improves the road segmentation accuracy and achieves good performance.

Object detection in very-high-resolution (VHR) remote sensing imagery remains a challenge. Environmental factors, such as illumination intensity and weather, reduce image quality, resulting in poor feature representation and limited detection accuracy. To enrich the feature representation and mine the underlying context information among objects, this article proposes a context-aware convolutional neural network (CA-CNN) model for object detection that includes proposal generation, context feature extraction, feature fusion, and classification. During feature extraction, we propose integrating a context-regions-of-interests (Context-RoIs) mining layer into the CNN model and extracting context features by mapping Context-RoIs mined from the foreground proposals to multilevel feature maps. Finally, the context features extracted from multilevel layers are fused into a single layer, and the proposals represented by the fused features are classified by a softmax classifier. In this article, through numerous experiments, we thoroughly explore the influence of key factors, such as Context-RoIs, different feature scales, and different spatial context window sizes. Because of the end-to-end network design approach, our proposed model simultaneously maintains high efficiency and effectiveness. We conducted all model testing on the public NWPU VHR-10 data set. The experimental results demonstrate that our proposed CA-CNN model achieves significantly improved model performance and better detection results compared with the state-of-the-art methods.

Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand‐crafted features, which are not sufficiently robust to a complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)‐based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy.

Automatically detecting airports from remote sensing images has attracted significant attention due to its importance in both military and civilian fields. However, the diversity of illumination intensities and contextual information makes this task difficult. Moreover, auxiliary features both within and surrounding the regions of interest are usually ignored. To address these problems, we propose a novel method that uses a multiscale fusion feature to represent the complementary information of each region proposal, which is extracted by constructing a GoogleNet with a light feature module model that has an additional light fully connected layer. Then, the fusion feature is input to a support vector machine whose performance is enhanced using a hard negative mining method. Finally, a simplified localization method is applied to tackle the problem of box redundancy and to optimize the locations of airports. An experiment demonstrates that the fusion feature outperforms other features on airport detection tasks from remote sensing images containing complicated contextual information.

In this paper, we focus on tackling the problem of automatic accurate localization of detected objects in high-resolution remote sensing images. The two major problems for object localization in remote sensing images caused by the complex context information such images contain are achieving generalizability of the features used to describe objects and achieving accurate object locations. To address these challenges, we propose a new object localization framework, which can be divided into three processes: region proposal, classification, and accurate object localization process. First, a region proposal method is used to generate candidate regions with the aim of detecting all objects of interest within these images. Then, generic image features from a local image corresponding to each region proposal are extracted by a combination model of 2-D reduction convolutional neural networks (CNNs). Finally, to improve the location accuracy, we propose an unsupervised score-based bounding box regression (USB-BBR) algorithm, combined with a nonmaximum suppression algorithm to optimize the bounding boxes of regions that detected as objects. Experiments show that the dimension-reduction model performs better than the retrained and fine-tuned models and the detection precision of the combined CNN model is much higher than that of any single model. Also our proposed USB-BBR algorithm can more accurately locate objects within an image. Compared with traditional features extraction methods, such as elliptic Fourier transform-based histogram of oriented gradients and local binary pattern histogram Fourier, our proposed localization framework shows robustness when dealing with different complex backgrounds.

Abstract Security in computer systems has been a major concern since the very beginning. Although security has been addressed in various aspects, accountability is one of the main facets of security that is lacking in today's computer systems. The ability not only to detect errors but also to find the responsible entity/entities for the failure is crucial. In this paper, we intend to provide a comprehensive investigation of the state‐of‐the‐art accountability research issues in current information systems. Also, we study the various accountability tactics that are available and how each one of them contributes to providing strong accountability of different aspects. Finally, we examine the various merits and tradeoffs. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we explore the malicious meter inspection (MMI) problem in neighborhood area smart grids. By exploiting a binary inspection tree, we propose a Difference-Comparison-based Inspection (DCI) algorithm to quickly target the malicious meters. Different from existing algorithms, the DCI algorithm is designed based on three rules that are derived according to the difference comparison results in each local subtree. An attractive feature of the DCI algorithm is that it manages to skip a large number of nodes on the binary inspection tree and thus accelerates the detection of malicious nodes. Both analysis and simulation results show that DCI outperforms the existing inspection algorithms in terms of inspection speed, regardless of the ratio and permutation of malicious meters.

Airports are one of the most important traffic facilities; thus, airport detection is of great significance in economic and military construction. This letter proposes a novel method for airport detection, with the entire algorithm based on line segment classification and texture classification. First, a fast line segment detector is applied to extract the line segments in images and compute the features of these line segments. Then, the line segments are discriminated by a trained runway line classifier, and the regions of interest (ROIs) are extracted from the line segments, which are classified as runway lines. Finally, whether the ROI is actually an airport is determined by analyzing the classification results of the image blocks. This method is unique in terms of the computing of line segment features and line segment classification. Experimental results demonstrate the effectiveness and robustness of the proposed method.

Although most research in lattice wireless sensor networks are focus on determining the optimal deployment pattern to provide full sensing coverage while maximizing the deployment efficiency, we study the partial sensing coverage problem and the corresponding node saving rate in 3D lattice WSNs. Two popular 3D deployment patterns including cube and triangular prism are considered. The partial sensing coverage and the note saving rate with respect to full sensing coverage are derived through mathematically modeling and theoretical analysis. Research results show that partial sensing coverage is of paramount significance to 3D lattice WSN design and implementation as a large amount of expensive 3D sensors can be saved by sacrificing a small amount of sensing coverage. For example, 38.43% and 22.14% sensors can be saved when providing 0.9864 and 0.9898 sensing coverage with respect to full sensing coverage in a cubic pattern and a triangular prism pattern based lattice WSN respectively. Computer-based simulations results validate the modeling and analysis.

Non-repudiation is one of the challenges in smart grids. A malicious smart meter is capable of compromising the power readings without being detected since it may be the only device to measure the electricity service amount in local. This kind of attack can cause financial loss due to incorrect readings and bring dispute between the power provider and the subscriber. In this paper, we address the non-repudiation problem with respect to accountability in the neighborhood area smart grids. We propose a mutual inspection strategy which can detect problematic smart meters and prevent further financial loss. Our evaluation results show that this scheme can be effectively applied to smart grids.

In this paper, we explore a quantitative approach to accountable wireless multi-hop networks. We propose using hierarchical P-Accountability to adapt the requirements of modeling a complex network environment and assess the degree of accountability in a fine-grained manner. We have defined P-Accountability and demonstrated its use in the hierarchical network environment. In addition, we apply P-Accountability to a wireless multi-hop network system. Both numerical and simulation results show that our approach is applicable to most accountable systems and that it provides a flexible and comprehensive view of the degree of accountability.