In the detection applications of synthetic aperture radar (SAR) data, a crucial problem is developing precise models for clutter statistics. Generalized gamma distribution (GΓD) has been widely applied in many fields signal processing, and it demonstrated to be an appropriate model describing statistical behaviors SAR sea clutter, wherein parameter estimation key issue determining practical application GΓD. Work that contains three major aspects performed this paper. First, approximate...

Hyperspectral anomaly detection (HAD) is regarded as an indispensable, pivotal technology in remote sensing and earth science domains. Nevertheless, most existing approaches for targets flatten 3-D hyperspectral images (HSIs) with spatial spectral information into 2-D vector data, which virtually breaks up the internal structure HSIs degenerates performance. To this end, we directly consider HSI data cube a tensor develop novel low-rank approximation (TLRA) algorithm to separate sparse...

Complex information in single-channel synthetic aperture radar (SAR) imagery is seldom used. This a common practice based on the conventional resolution theory. However, with advent of high-resolution SAR sensors, complex data has been found to be significance for ocean applications. In particular, we note that there special type instrumental artifact Sentinel-1 images. It rarely researched and may attributed radio frequency interference (RFI). similar intensity ships can degrade...

The simple linear iterative clustering (SLIC) method is a recently proposed popular superpixel algorithm. However, this may generate bad superpixels for synthetic aperture radar (SAR) images due to effects of speckle and the large dynamic range pixel intensity. In paper, an improved SLIC algorithm SAR proposed. This exploits likelihood information image clusters. Specifically, local scheme combining intensity similarity with spatial proximity Additionally, post-processing, edge-evolving that...

Recent advances in sensor based human activity recognition (HAR) have exploited deep hybrid networks to improve the performance. These models combine Convolutional Neural Networks (CNNs) and Recurrent (RNNs) leverage their complementary advantages, achieve impressive results. However, roles associations of different sensors HAR are not fully considered by these models, leading insufficient multi-modal fusion. Besides, commonly used RNNs suffer from 'forgetting' defect, which raises...

Many countries are making increased efforts to improve marine security and safety develop ship surveillance techniques satisfy the increasing demands. Space-borne Synthetic Aperture Radar (SAR) delivers high performance day/night all weather capabilities a space-based Automatic Identification System (AIS) can give near real time global coverage. Limited by development of sensors data processing techniques, integration space-borne SAR AIS has much offer surveillance. State-of-the-art fusion...

The product of multilook amplitudes (PMA) detector has been used to detect ships in high-resolution dual-polarization synthetic-aperture-radar images. However, the adaptive constant false-alarm rate (CFAR) technique PMA is desirable for practical applications, wherein a crucial problem find an appropriate model describe statistics varied sea surfaces. First, we consider new probability density function characterize homogeneous Second, by using and multiplicative model, detector's statistical...

Recent advances in time series classification (TSC) have exploited deep neural networks (DNN) to improve the performance. One promising approach encodes as recurrence plot (RP) images for sake of leveraging state-of-the-art DNN achieve accuracy. Such an has been shown impressive results, raising interest community it. However, it remains unsolved how handle not only variability distinctive region scale and length sequences but also tendency confusion problem. In this paper, we tackle problem...

Feature extraction is an important step for target classification in SAR images. Principal component analysis (PCA) common pattern recognition, and has been used widely In order to utilize PCA, two-dimensional image be arranged observation vector. However, PCA (2D-PCA), which developed from can extract features directly. Although 2D-PCA consistent with theory essentially, represents original data by extracting principal components high variance values linear transformation, they perform...

A new feature named '2D comb' to improve ship classification is proposed. The proposed presents added value distinguish between container ship, tank and cargo ship. It based on radar cross-section (RCS) statistic of the target related structure. Besides, local RCS classify three kinds ships. Experimental results TerraSAR-X images show that can abstract describe structure in high-resolution synthetic aperture (SAR) imagery. establishes relationship structure, which very useful different

Bilateral filtering is a technique to smooth images while preserving edges; it employs both geometric closeness and intensity similarity of neighbouring pixels. When pixels very high, however, bilateral weakens into Gaussian filtering. The performance does not improve significantly the computation still expensive. Many existing accelerated algorithms, ignored this basic fact. In study, hybrid algorithm based on edge detection proposed. By making use detection, proposed combines its degree...

Visual place recognition (VPR) in changing environments is an urgent challenge for long-term autonomous navigation. One recent ConvNet landmark-based approach exploits region landmarks coupled with features to match images, and the has shown promising results under significant environmental viewpoint changes. In this paper, we propose a robust system VPR outdoor roadway by extension of from following two aspects. First, our method utilizes more discriminative obtained novel refinement called...

A constant false alarm rate (CFAR) detecting method for ships in high-resolution dual-polarization synthetic aperture radar (SAR) amplitude images has been proposed this paper. First, by the production of from two polarimetric channels, a novel detector simply called PMA constructed. We testified that could improve signal-to-clutter ratio (SCR) and make discrimination ship clutter more easily. Second, detector&#x2019;s statistical model described well-known <svg...

Efficient and robust visual localization is important for autonomous vehicles. By achieving impressive accuracy under conditions of significant changes, ConvNet landmark-based approach has attracted the attention people in several research communities including Such an relies heavily on outstanding discrimination power features to match detected landmarks between images. However, a major challenge this how extract discriminative efficiently. To address challenging, inspired by high...

Target birth intensity, which plays a role similar to track initialisation, is an important part of the probability hypothesis density (PHD) filter. In most papers, intensity always known as priori, but it too restrictive for real application. Besides, existing algorithms estimation only consider measured component target state (e.g. position), unmeasured velocity) viewed priori or modelled by simple distribution. As result, they are not efficient enough represent initial states newborn...

The polysaccharides extracted from Aspidopterys obcordata are thought to have anti-urolithiasis activity in Drosophila kidney stones. This study aimed assess the effects of different extraction solvents on yield, chemical composition, and bioactivity A. obcordata. were by using four solutions: hot water, HCl solution, NaOH 0.1 M NaCl. results revealed that significantly influenced yields, molecular weight distribution, monosaccharide compositions, preliminary structural characteristics,...

This paper proposes an algorithm of simulating spatially correlated polarimetric synthetic aperture radar (PolSAR) images based on the inverse transform method (ITM). Three flexible non-Gaussian models are employed as underlying distributions PolSAR images, including KummerU, W and M models. Additionally, spatial correlation texture component is considered, which described by a parametric model called anisotropic Gaussian function. In algorithm, simulated multiplying two independent...