The detection of insulators with cluttered backgrounds in aerial images is a challenging task for an automatic transmission line inspection system. In this paper, we propose effective and reliable insulator method based on deep learning technique images. the proposed approach, single shot multibox detector (SSD), powerful meta-architecture, incorporated strategy two-stage fine-tuning. SSD-based model can realize multi-level feature extractor from instead manually extracting features....

Identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel matching method (named R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> FD ) that robust to rotation differences, which consists repeatable detector rotation-invariant descriptor. In first stage, called Multi-channel...

The registration of synthetic aperture radar (SAR) and optical images is a challenging task due to the potential nonlinear intensity differences between two images. In this paper, novel image method, which combines diffusion phase congruency structural descriptor (PCSD), proposed for SAR First, reduce influence speckle noise on feature extraction, uniform diffusion-based Harris (UND-Harris) extraction method designed. UND-Harris detector developed based diffusion, proportion, block strategy,...

The scale-invariant feature transform (SIFT) algorithm has been widely applied to optical image registration. However, mostly because of multiplicative speckle noise, SIFT a limited performance when directly synthetic aperture radar (SAR) image. In this letter, novel SAR registration method is proposed, which based on the combination SIFT, nonlinear diffusion, and phase congruency. our proposed algorithm, multiscale representation generated by since it better preserves edges in as opposed...

Deep learning-based change detection (CD) using remote sensing images has received increasing attention in recent years. However, how to effectively extract and fuse the deep features of bi-temporal for improving accuracy CD is still a challenge. To address that, novel adjacent-level feature fusion network with 3D convolution (named AFCF3D-Net) proposed this article. First, through inner property convolution, we design new way that can simultaneously information from images. Then, alleviate...

Over the past few decades, with rapid development of global aerospace and aerial remote sensing technology, types sensors have evolved from traditional monomodal (e.g., optical sensors) to new generation multimodal multispectral, hyperspectral, light detection ranging (LiDAR), synthetic aperture radar (SAR) sensors). These advanced devices can dynamically provide various abundant images (MRSIs) different spatial, temporal, spectral resolutions according application requirements. Since then,...

With the expansion of optical and SAR image fusion application scenarios, it is necessary to integrate their information in land classification, feature recognition, target tracking. Current methods focus excessively on integrating multimodal enhance richness fused images, whereas neglecting highly corrupted visual perception results by modal differences speckle noise. To address that, this paper proposes a novel framework named Visual Saliency Features Fusion (VSFF), which based extraction...

Automatic registration of synthetic aperture radar (SAR) and optical images is still a challenging problem because the potential differences in geometric intensity. In this work, we propose robust efficient method for improving performance SAR images. Our work consists mainly two steps, including feature point detection stage description stage. first stage, present new extraction method, named nonlinear diffusion-based Harris-Laplace (NDHL) detector, which incorporates diffusion spatial...

The structural features using self-similarity have become more popular for multimodal remote sensing image matching. However, mostly because of significant geometric distortions and nonlinear intensity differences between images, these methods produce a limited matching performance when directly applied to images. To address that, we propose novel feature descriptor named pyramid orientated (POSS) matching, which integrates phase congruency (PC) into the model better encoding information....

In this paper, we propose a new image registration method for synthetic aperture radar (SAR) with multiscale patch features, in which the sparse representation technique is exploited. Considering influence of speckle noise on feature extraction, proposed method, spatial correlation strategy based stationary wavelet transform adopted to select reliable points from initial scale invariant keypoints reference image. By introducing patch, descriptor further designed describe attribute domain...

Image registration is an important preprocessing step in many synthetic aperture radar (SAR) image applications. A key issue to reliably establish the correspondences between feature points extracted from reference and sensed images. new point matching algorithm proposed this paper align two SAR In method, by considering patches as basic units, a novel local descriptor including intensity geometric information assigned each point, which more robust speckle noise. Furthermore, correspondence...

Cellular vehicle-to-everything (C-V2X) communication, as a part of 5G wireless communications, has been considered one the most significant techniques for Smart City. Vehicles platooning is an application City that improves traffic capacity and safety by C-V2X. However, different from vehicles travelling on highways, C-V2X could be more easily eavesdropped spectrum resource limited when converge at intersection. Satisfying secrecy rate C-V2X, how to increase efficiency (SE) energy (EE) in...

Building change detection plays an imperative role in urban construction and development. Although the deep neural network has achieved tremendous success remote sensing image building detection, it is still fraught with problem of generating broken boundaries separation dense buildings, which tends to produce saw-tooth boundaries. In this work, we propose a feature decomposition-optimization-reorganization for detection. The main contribution proposed that performs by respectively modeling...

Automatic and precise matching between optical synthetic aperture radar (SAR) images is still a challenging task because of significant radiation texture differences such images. Recently, structure feature-based methods are popular for the SAR However, current descriptors include many noninformative features, which degrade their performance. To address that, we present robust method by multiscale masked feature representation. We first extract pixelwise gradient features on multiple scales...

The salient region, which is a basic feature in the early stage of human vision system, has been utilized to solve problems image analysis and interpretation nowadays. Although there are several salient-region detection methods for optical images, it hard work synthetic aperture radar (SAR) large multiplicative speckle noise. Based on statistical distribution noise local intensity variation, this letter presents novel multiple-scale method SAR images. In method, via constructing 2-D...

Currently, salient-region detection, which can be used to provide important man-made target information for synthetic aperture radar (SAR) image analysis and interpretation, is a valuable tool in SAR processing. However, because images are blurred by large amount of multiplicative speckle noise, it difficult existing methods produce satisfying results with simple intensity variation. Based on the anisotropic scale space, describe edge variations, this letter presents new detection method...

Automatic matching of synthetic aperture radar (SAR) and optical images is a fundamental task in many remote sensing applications. However, due to different imaging modalities, conventional methods provide limited performances. In this letter, based on the observation that structural features are maintained across modality images, we propose novel feature-based method effectively address SAR image matching. The proposed built phase congruency (PC) model consists mainly two stages. First,...

In this letter, a novel method for change detection is proposed using neighbourhood structure correlation. Because features are insensitive to the intensity differences between bi-temporal images, we perform correlation analysis on rather than information. First, extract feature maps by multi-orientated gradient Then, used obtain Neighbourhood Structural Correlation Image (NSCI), which can represent context addition, introduce measure named matching error, be improve Subsequently, model...

Accurate registration of optical and synthetic aperture radar (SAR) images remains a challenging task because the potential large modality differences across individual images. To improve performance, this paper proposes robust method for SAR based on novel multi-orientation relative total variation (MORTV) structural representation. The MORTV model is designed by integrating multiple orientation strategy into original RTV to extract maps, which can capture more features while removing image...

Establishing feature correspondences between multimodal remote sensing images is an essential task for realizing diverse applications. Conventional matching methods, which employ gradient or phase congruency (PC) detection and description, produce limited performance when suffer from strong noises intensity differences. In this study, we propose a novel modality-invariant structural representation (MISFR) method image matching. First, maximal/minimal enhanced PC moment (EPCM) designed by...

Multisensor image fusion can be used as an advanced technique for enhancement. We propose a synthetic aperture radar (SAR) and visible method under the framework of variational multiscale decomposition (VMID) model. In proposed method, two rules are respectively designed to fuse structure texture components obtained with VMID model source images. A rule based on curvelet transform is employed fusing component local energy criterion adopted construct component. Moreover, considering influence...

The recently proposed triplet Markov random fields (TMF) model is very suitable for dealing with non‐stationary image segmentation. However, influenced by multiplicative speckle noise, synthetic aperture radar (SAR) dim and blurred in the boundaries of different areas, making it difficult to locate boundary accurately segmentation process. Thus, this study, authors propose a new algorithm using fuzzy label field‐based TMF SAR images. In algorithm, value each site field extended from finite...