Common feature representation in optical and synthetic aperture radar (SAR) image registration is one of the most challenging tasks due to the significant geometric and radiometric differences. This letter proposed aProminent structure-guided feature (PSGF)representation for SAR and optical image registration. Firstly, the prominent structure of the image is highlighted based on windowed inherent variations, which is conducive to identifying more accurate and reliable corresponding points. Secondly, the maximum response index filter banks are proposed to extract structure features with multi-orientation convolution results. Then the structure feature-guided representation generated from this filtering map is quantized in histograms. Finally, the descriptor with radiation invariance is employed for feature matching, enabling automatic image registration with high accuracy. Comparative analysis with state-of-the-art methods on diverse terrain data demonstrates the superiority of the proposed PSGF method for SAR and optical image registration.

Accurate annotation of construction disturbance outline greatly influences the evaluation quality in interpretation application. While the loss functions used in semantic segmentation nowadays are difficult to reflect the contour information. Therefore, this paper proposes a RA U-Net(Residual Attention U-Net) construction disturbance interpretation. The model uses an exactly defined Contour Loss(CL) as the loss function. This function could reflect the local segmentation information of each element by extracting and transferring contour features into the weight matrix. Moreover, this paper modifies the CL function to make it suitable for construction disturbance interpretation. The experiments on M-nih Massachusetts Building Dataset and Construction Disturbance Dataset labeled manually show the capability of the proposed function in remote sensing interpretation. The proposed model has been used for the Soil and Water Conservation project supervised by the local government. And the IoU increased 1.85% maximum than binary cross-entropy(BCE) on Mnih Massachusetts Building Dataset.

Images registration for optical and synthetic aperture radar (SAR) is a key of multi-sensor images analysis. And parameters selection affects the final result of the registration algorithms for optical and SAR images. For images obtained by different sensors, how to choose an appropriate parameter for accurate registration is a key problem. In this letter, a parameter adaptive registration algorithm between optical and SAR images based on SIFT is proposed. Because of the different imaging mechanisms of these two kinds of images, the algorithm uses the multiscale Sobel operator to calculate the gradient for the optical image, while for the SAR image, a new adaptive operator based on the neighborhood pixel value is proposed to calculate the gradient. In the feature extraction, the adaptive value estimated by constant false alarm rate (CFAR) detection is used instead of the fixed threshold. Finally, a matching method constrained by image size scaling (Scale-Constrained Fast Sample Consensus) is proposed. The evaluation was designed in two aspects: feature extraction and image registration. The algorithm we proposed shows excellent performances in the aspects of repeatability, correct matching rate and root mean square error. The experimental results show that our method maintains the performance of the original algorithm and has some optimization and breakthrough.

In recent years, vehicular named data networking (VNDN) has quickly ascended to the spotlight and gained enormous popularity, which has emerged as a candidate to support various applications of vehicular communications. VNDN has the potential improve the data dissemination efficiency by mitigating the performance degradation from Internet Protocol (IP) addressing, unstable connectivity and diversified service requirements. With the number of connected vehicles increasing rapidly, the traffic burden of the base station (BS) also grows. As an effective edge computing paradigm, in-vehicle caching can significantly relieve the pressure of the BS. However, the design of a fair caching strategy is still challenging due to the selfish nature of individuals. In this paper, to address the above issues, a popularity-incentive caching scheme (PICS) is proposed in VNDN, where the BS will reward vehicles who execute cache offloading and content sharing with others. To balance the conflict of interest between the BS and vehicles, a Stackelberg game is modeled with rational utilities envisioned. Next, we propose the solution of this game model and evaluate the influence of different weight parameters. Finally, simulation results validate the effectiveness of PICS.

Semantic segmentation could not distinguish the spot's contour, which has both the construction disturbance region and original physiognomy. This paper proposed a semantic segmentation network named Residual Attention U-Net (RA U-Net) in the appliance of construction disturbance interpretation. With Inception-v3 as the backbone, the proposed model used the residual attention module replaced the skip connection in U-Net and Conditional Random Field (CRF) as the post-processing. Regarding natural landform as noise, the residual attention module could retain the natural landform. Then, CRF was used to fine-grained outline. The experiment on Standford Background Dataset proved the capability of the proposed model in semantic segmentation. It shows a good performance in the construction disturbance interpretation dataset labelled by ourselves. Moreover, it has been used for the Soil and Water Conservation project held by the local government.

In this paper, a Generative Adversarial Network(GAN) is proposed for data augmentation of remote sensing images abstracted from Jiangsu province in China, i.e., D-sGAN(Deeply-supervised GAN). At First, to modulate the layer activations, a down-sampling scheme is designed based on the segmentation map. Then, the architecture of the generator is UNet++ with the proposed down-sampling module. Next, the generator of this net is deeply supervised by the discriminator using deep Convolutional Neural Network(CNN). This paper further proved that the proposed down-sampling module and the dense connection characteristics of UNet++ are significantly beneficial to the retention of semantic information of remote sensing images. Numerical results demonstrated that the images generated by D-sGAN could be used to improve accuracy of the segmentation network, with a better Fully Convolutional Networks Score(FCN-Score) compared to the GoGAN, SimGAN and CycleGAN models.

Vehicular named data networking (VNDN) has recently emerged as a novel paradigm to facilitate content-centric data sharing for Internet of Vehicles. However, an information holder can spread fake data to clients for malicious purposes, which may affect the driving decision of the recipient, or even worse, cause traffic congestion and accidents. In this article, we build a data-sharing system that consists of a double-layer blockchain. The nodes at the bottom layer request for service by announcing their requirements in the NDN paradigm. For the upper layer, the nodes submit their demands and supplies to the nearest roadside unit for further matching. We model the balance between the demand and supply as a matching game. To encourage nodes to provide positive services, a reputation management mechanism that combines negative and positive transaction records is proposed. Simulation results verify the validity of our system, and the data-sharing mechanism fosters a secure information interaction in the VNDN.

To improve the urban traffic condition and reduce accidents, we propose a platoon-driving model for autonomous vehicles in a free-flow traffic state in this article. This model allows vehicles with successful path matching to be grouped in a platoon and led by the platoon head (PH). In addition, a PH selection scheme is introduced to provide an incentive for vehicles to be PHs and maintain the dynamic update of platoons. Next, a smart contract is employed to enable the payment based on a blockchain between the PH and platoon members (PMs), avoiding the malicious and false payments. The numerical results show that the platoon model is superior to the individual driving model in terms of fuel consumption. The comparison between carpooling and noncarpooling modes within the platoon shows that our model has a better performance in terms of PH revenue and PM's service charge.

Image segmentation based on superpixel is used in urban and land cover change detection for fast locating region of interest. However, the segmentation algorithms often degrade due to speckle noise in synthetic aperture radar images. In this paper, a feature learning method using a stacked contractive autoencoder (sCAE) is presented to extract the temporal change feature from superpixel with noise suppression. First, an affiliated temporal change image, which obtains temporal difference in the pixel level, are built by three different metrics. Second, the simple linear iterative clustering algorithm is used to generate superpixels, which tightly adhere to the change image boundaries for the purpose of acquiring homogeneous change samples. Third, a sCAE network is trained with the superpixel samples as input to learn the change features in semantic. Then, the encoded features by this sCAE model are binary classified to create the change result map. Finally, the proposed method is compared with methods based on principal components analysis and Markov random fields. Experiment results show that our deep learning model can separate nonlinear noise efficiently from change features and obtain better performance in change detection for synthetic aperture radar images than conventional change detection algorithms.

Based on the analysis to the phenomenon and methods of denial-of-sleep attacking in wireless sensor network, a scheme is proposed employing fake schedule switch with RSSI measurement aid. The sensor nodes can reduce and weaken the harm from collision, exhaustion and broadcast attack and on the contrary make the attackers lose their energy quickly so as to die. Simulation results show that at a bit price of energy and delay, network health can be guaranteed and packets drop ratio has been decreased compare with original scenario without our scheme.