Despite many state-of-the-art methods have been proposed for infrared (IR) small target detection, target detectability (TD) and background suppressibility (BS) cannot be significantly improved simultaneously, especially in complex situations. This paper proposes a novel IR small target detection method named improved multi-mode nuclear norm joint local weighted entropy contrast (IMNN-LWEC), which represents the IR target detection task as an optimization problem for tensor decomposition of three components in spatial-temporal domain, including background tensor, target tensor and sparse structure tensor. First, to utilize the spatial and temporal information in an IR sequence effectively, we transform the original IR sequence into a nonoverlapping spatial-temporal patch-tensor. Second, a nonconvex approximation of tensor rank called improved multi-mode weighted tensor nuclear norm (IMWTNN) is proposed to estimate background tensor rank, which is of benefit to separate the background component more completely from the original image. Third, based on structure tensor theory, we introduce a new sparse prior map called local weighted entropy contrast (LWEC) via a designed image entropy operator and a new prior information filter, which can further preserve the target and suppress the background simultaneously. Besides, a novel tube-wise sparse regularization term is designed to identify linear sparse structures. And Frobenius norm is used to characterize noise. Finally, to solve the proposed model, an efficient optimization scheme utilizing the alternating direction multiplier method (ADMM) is designed to retrieve the small targets. Comprehensive experiments on five datasets witness the superior TD and BS performance of the proposed method compared with nine state-of-the-art detection methods.

Due to significant geometric distortions and illumination differences, developing techniques for high precision and robust multisource remote sensing image registration poses a great challenge. This article presents an iterative image registration approach, called iterative scale-invariant feature transform (ISIFT) for remote sensing images, which extends the traditional scale-invariant feature transform (SIFT)-based registration system to a close-feedback SIFT system that includes a rectification feedback loop to update rectified parameters in an iterative manner. Its key idea uses consistent feature point sets obtained by maximum similarity to calculate new alignment parameters to rectify the current sensed image and the resulting rectified sensed image is then fed back to update and replace the current sensed image as a new sensed image to reimplement SIFT for next iteration. The same process is repeated iteratively until an automatic stopping rule is satisfied. To evaluate the performance of ISIFT, both the simulated and real images are used for experiments for the validation of ISIFT. In addition, several data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. Furthermore, experiments with different rotation are also performed to verify the adaptability of ISIFT under different rotation distortions. The experimental results demonstrate that ISIFT improves performance and produces better registration accuracy than traditional SIFT-based methods and existing state-of-the-art methods.

Low-rank and sparsity-matrix decomposition (LRaSMD) has received considerable interests lately. One of effective methods for LRaSMD is called go decomposition (GoDec), which finds low-rank and sparse matrices iteratively subject to the predetermined low-rank matrix order m and sparsity cardinality k. This article presents an orthogonal subspace-projection (OSP) version of GoDec to be called OSP-GoDec, which implements GoDec in an iterative process by a sequence of OSPs to find desired low-rank and sparse matrices. In order to resolve the issues of empirically determining p = m+ j and k, the well-known virtual dimensionality (VD) is used to estimate p in conjunction with the Kuybeda et al. developed minimax-singular value decomposition (MX-SVD) in the maximum orthogonal complement algorithm (MOCA) to estimate k. Consequently, LRaSMD can be realized by implementing OSP-GoDec using p and k determined by VD and MX-SVD, respectively. Its application to anomaly detection demonstrates that the proposed OSP-GoDec coupled with VD and MX-SVD performs very effectively and better than the commonly used LRaSMD-based anomaly detectors.

Low-rank and sparse representation (LRaSR)-based approaches have been widely used for anomaly detection (AD). Their central ideas are to minimize the rank of the low-rank space constrained to predetermined values, while using various regularization parameters to control the sparse representation. Three key issues arise from LRaSR. The first is how to determine the constrained rank. The second is an appropriate selection of regularization parameters. The third one is the detector used for AD. This article presents a new but rather simple competing model, called component decomposition analysis (CDA) which represents a data space X as a linear orthogonal decomposition of three components, X = PC $^{{m}} +$ IC $^{{j}} +$ N with ${m}$ principal components, PC $^{{m}}$ , generated by principal component analysis (PCA) and ${j}$ independent components, IC $^{{j}}$ , generated by independent component analysis (ICA) plus a noise component N. CDA offers several advantages over LRaSR. First, CDA uses well-known component analysis techniques to decompose the dataset without solving constrained optimization problems. Second, the values of ${m}$ and ${j}$ can be automatically determined by virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD). To better extract anomalies from the IC $^{{j}}$ component space, the concept of sparsity cardinality (SC) is further incorporated into CDA to derive a CDASC anomaly detector (CDASC-AD). The experimental results demonstrate that CDASC-AD is very competitive against the LRaSR-based models and performs well in hyperspectral AD.