Search

Synthetic aperture radar (SAR) radiometric calibration is an essential step in inferring meaningful physical information about the target. The majority of current calibration processes are directly based on artificial calibrators, but the number of calibrators is always restricted. Cross calibration, which has been widely used for optical and meteorological satellites, performs calibration by another calibrated satellite. Distributed targets are used as the calibration reference, allowing for frequent calibration. However, the potential of introducing cross calibration to SAR satellites has not been confirmed, and two issues must be resolved: 1) the number of calibration targets available for cross calibration is limited to a few known distributed targets; 2) the imaging parameters of calibrated and uncalibrated SARs are different, which may cause errors when cross calibration is applied. In this article, a method for selecting stable distributed targets using time-series stability analysis was presented first. The saline–alkali land and urban areas were selected as the calibration targets with low and high backscattering, respectively. Second, different stable pixel extraction methods were adopted based on the characteristics of different targets. Third, the Oh model was used to correct the scattering difference caused by the incidence angle difference, which greatly improved the accuracy of the backscattering coefficients of the calibration targets. The cross calibration experiments on the same series of satellite (Sentinel-1 A/B) data revealed that the accuracy of cross calibration was comparable to the accuracy of the artificial calibrator-based method, with a difference of less than 0.48 dB (1$\sigma$).

Parameter estimation of Lorenz chaotic system using a novel hybrid Jaya-Powell algorithm is proposed in this paper. Since the nonlinear dynamic system is complex with multi-dimension parameters, estimating parameters of the system can be considered as a muti-objective optimization task. The proposed Jaya-Powell algorithm combines the Jaya and Powell algorithm to search for the relatively global optimum and local optimum respectively, which offers a more accurate and effective estimation. The searching strategy of the proposed algorithm facilitates the balance of the exploration and exploitation in the optimization procedure. Due to no algorithm-specific parameters are required in the Jaya and Powell algorithm, the proposed Jaya-Powell can avoid deliberate fine-tuning of corresponding parameters. To validate the accuracy and robustness of the proposed algorithm in parameter estimation, the simulation of Lorenz chaotic system and comparative experiments are conducted. Seven algorithms, including Jaya algorithm, Powell algorithm, Teaching-learning-based optimization (TLBO) algorithm, particle swarm optimization (PSO), genetic algorithm (GA), covariance matrix adaptation evolution strategy (CMA-ES), and cluster-chaotic-optimization algorithm (CCO), are considered as benchmarking algorithms in the comparison. The proposed hybrid Jaya-Powell algorithm outperforms seven benchmarking algorithms with the more accurate estimation and the relatively faster convergence. Based on the embedded system Raspberry pi 3, the proposed algorithm achieves the similar performance by comparing with the experiments conducted on the computer. The successful implementation via Raspberry pi 3 facilitates the application of the proposed algorithm in edge computing.

Anomaly detection is an active topic in hyperspectral image processing. Recently, low-rank representation (LRR)-based approaches have shown satisfactory results in wide anomaly detection applications. However, the existing LRR methods still have the following two problems: 1) setting a fixed value as a termination condition of the iterative constraint often results in the loss of target information, leading to a low detection rate with some missing targets, and 2) noise after LRR still remains in the sparse part, which increases false alarms. This article proposes the tensor approximation with LRR and the kurtosis correlation constraint (TAKCC) method for anomaly detection. The hyperspectral image is regarded as a third-order tensor for the LRR process. In the optimization process, the background suppression degree is obtained through the background dictionary to determine the iteration termination condition. After the iterative optimization is completed, the low-rank tensor that can fully represent the background is obtained. Also, the difference between the original hyperspectral image tensor and the low-rank tensor is used as the input of the kurtosis correlation constraint. The kurtosis correlation constraint compares the similarity between the current pixel and its surrounding pixels to detect the anomaly, where the kurtosis in the high-order statistical feature is introduced to avoid the interference of noise. The experimental results illustrate that the proposed method can retain the complete target information to highlight targets while suppressing background. [ABSTRACT FROM AUTHOR]

In recent years, correntropy has been successfully applied to robust adaptive filtering to eliminate adverse effects of impulsive noises or outliers. Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables. This definition is reasonable when the error between the two random variables is symmetrically distributed around zero. For the case of asymmetric error distribution, the symmetric Gaussian kernel is however inappropriate and cannot adapt to the error distribution well. To address this problem, in this brief we propose a new variant of correntropy, named asymmetric correntropy, which uses an asymmetric Gaussian model as the kernel function. In addition, a robust adaptive filtering algorithm based on asymmetric correntropy is developed and its steady-state convergence performance is analyzed. Simulations are provided to confirm the theoretical results and good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Anomaly detection in a hyperspectral image (HSI) has been actively researched in the field of remote sensing due to its significant application requirements. Traditional methods were based on the spatial models for the background to detect the anomaly targets. However, in detecting the spectral anomaly targets, they led to two problems: 1) the spatial characteristics of spectral anomaly targets are not obvious, which causes many false alarms in detection and 2) spectral anomaly usually occurs in the local band of targets, while the rest of the spectrum is similar to the ones of surrounding backgrounds, which leads to missed detection. This article proposes a novel hyperspectral anomaly detection method for spectral anomaly targets based on spatial and spectral constraints (SASCs). This model finds suspected anomaly target part as spatial anomaly results through SASCs. Then, the feedback process determines the spectral anomaly through the spectral difference between the tested pixel and the surrounding background. It is fed back to the spatial anomaly results to obtain final detection results. Furthermore, in order to enlarge the spectral difference between anomaly and background while suppressing the background, the optimal order of fractional Fourier transform (FrFT) is determined by combining spatial anomaly results with the uncertainty principle, which is used in FrFT of HSI. Experimental results show that the proposed method suppresses the background and reduces the false alarm rate. The feedback mechanism effectively reduces the missing detection rate, achieving a promising detection accuracy. [ABSTRACT FROM AUTHOR]