Infrared (IR) small target detection problem has attracted increasing attention. Tensor theory-based detection techniques have been widely utilized, while facing challenges such as tensor structures, background and target estimation. This paper proposes an IR dim and small target detection method based on 5-D spatial-temporal knowledge (5D-STD). Specifically, a 5-D whitened spatial-temporal patch-tensor is constructed. Then, we design a 5-D tensor nuclear norm for background estimation and a Moreau envelope-derived sparsity estimation norm. Furthermore, we establish a low-rank and sparse decomposition model with an alternating direction method of multipliers (ADMM)-based optimization scheme for IR target detection. Extensive experiments conducted on three real IR sequences prove the superiority of 5D-STD in terms of target detectability, background suppressibility and overall performance.
KEYWORDS: Target detection, Small targets, Infrared imaging, Infrared detectors, 3D modeling, 3D acquisition, Infrared radiation, Detection and tracking algorithms, Thermal modeling, 3D image processing
Infrared (IR) small target detection has been widely used in civilian and military applications. Although low-rank and sparse tensor decomposition theory has been widely employed, the estimations of target and background are still not precise enough. This paper proposes an IR small target detection method based on improved clustering and Bayesian guided-tracking regularization (STD-ICBT). Specifically, a 3-D spatial-temporal tensor is constructed first. Secondly, we improve the K-means clustering algorithm for lower-rank background fiber clusters and design an improved K-means clustering-based background estimation method, making it more accurate for background estimation. Furthermore, we design an efficient ADMM-based optimization algorithm for solving the target detection model. Compared with six state-of-the-art competitive methods, it demonstrates the superiority of STD-ICBT in terms of target detectability (TD), background suppressibility (BS), and overall performance
We present a general half-quadratic based hyperspectral unmixing (HU) framework to solve the robust or sparse unmixing problem. A series of potential methods can be designed and developed to solve HU problem through this framework. By introducing correntropy metric, a correntropy based spatial-spectral robust sparsity regularized (CSsRS-NMF) unmixing method is derived through the proposed framework to achieve two-dimensional robustness and adaptive weighted sparsity constraint for abundances simultaneously.
Recently, many state-of-the-art methods have been proposed for infrared (IR) dim and small target detection, but the performance of IR small target detection still faces with challenges in complicated environments. In this paper, we propose a novel IR small target detection method named local entropy characterization prior with multi-mode weighted tensor nuclear norm (LEC-MTNN) that combines local entropy characterization prior (LEC) and multi-mode weighted tensor nuclear norm (MTNN). First, we transform the original infrared image sequence into a nonoverlapping spatial-temporal patch-tensor to fully utilize the spatial and temporal information in image sequences. Second, a nonconvex surrogate of tensor rank called MTNN is proposed to approximate background tensor rank, which organically combines the sum of the Laplace function of all the singular values and multi-mode tensor extension of the construct tensor without destroying the inherent structural information in the spatial-temporal tensor. Third, we introduce a new sparse prior map named LEC via an image entropy characterization operator and structure tensor theory, and more effective target prior can be extracted. As a sparse weight, it is beneficial to further preserve the targets and suppress the background components simultaneously. To solve the proposed model, an efficient optimization scheme utilizing the alternating direction multiplier method (ADMM) is designed to retrieve the small targets from IR sequence. Comprehensive experiments on four IR sequences of complex scenes demonstrate that LEC-MTNN has the superior target detectability (TD) and background suppressibility (BS) performance compared with other five state-of-the-art detection methods.
This paper develops an independent component analysis (ICA) with subspace projection for hyperspectral anomaly detection. The proposed model represents the sphered data set X as an ICA model specified by a two-component orthogonal sum decomposition, X IC N = +j with j independent components, j IC , generated by ICA and a noise component N. To better extract anomalies from the j IC component space, the concept of sparsity cardinality (SC) is integrated into ICA to derive a ICASC anomaly detector (ICASC-AD). For determine appropriate values of j, the virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD) are used for this purpose. The experimental results demonstrate that ICASC are very competitive against the LRaSR-based models in hyperspectral anomaly detection.
Nowadays, the low-rank representation (LRR) and deep learning-based methods have received much attention in anomaly detection for hyperspectral images (HSIs). However, most of these methods mainly focus on the powerful reconstruction capability of the neural networks while ignoring the potential probability distribution of both anomalies and background pixels. To solve the problem, we propose a sparse component extraction-based probability distribution representation detector (SC-PDRD) framework, which integrates the characteristic of the sparse component obtained by the LRR model with the powerful probability representation ability of the variational autoencoder (VAE) network. The LRR model effectively separates the anomaly component from the background, which also serves as the prior anomaly distribution for each pixel. Moreover, the VAE architecture tries to recover the potential anomaly distribution using the sparse detection map in the feature space. In addition, we employ the Chebyshev neighborhood to leverage spatial information. The modified Wasserstein distance measures the distance between the test pixel and its neighborhood. The final detection map is attained by combining the prior anomalous degree of the anomalies with the output of the VAE network. Experimental results on three real HSIs demonstrate the effectiveness and superiority of SC-PDRD.
To achieve registration of multi-sensor images by utilizing complementary information, this paper proposes an iterative image registration method based on scale invariant feature transformation (SIFT) and extended phase correlation (EPC), named as SIFT_IEPC. The reference image and the sensed image are pre-registered by SIFT and a geometrical outlier removal method. Overlapping regions corresponding to the reference image and the rectified sensed image are partitioned to block image with equal size, and the extended phase correlation is used to estimate the translation parameters between each block pairs, which are used to tune the matched feature point pairs in the block. The tuned feature point sets are used to update the registration parameters between the reference image and the sensed image. Repeat the process of EPC matching and feature tuning until terminate condition is satisfied. Experiments on three pairs including simulated and real remote sensing images are conducted to evaluate the performance of SIFT_IEPC. The comparison experiments demonstrate that SIFT_IEPC can apparently increase the accuracy of image registration.
Spectral variability is an inevitable problem in spectral unmixing. The linear mixing model (LMM) is often used due to its simplicity and mathematical tractability. Unfortunately, the linear mixing assumption is not always true in many real scenarios. To address this issue, we adopt a variant of the LMM to take care of spectral variability, called scaled and perturbed LMM, which can be used to constrain modeling reflectance scaling caused by topography or illumination, and simulating irregular spectral variabilities. To facilitate effective optimizations of the variables, a few regularizations are employed to regularize the introduced constraints and an alternating direction method of multipliers algorithm is further used to optimize all the variables of this model. Experimental results obtained from a synthetic dataset and two real datasets demonstrate that the proposed approach outperforms other algorithms in unmixing hyperspectral images with spectral variabilities.
The accuracy of two sets of feature points is significant to remote sensing image registration based on feature matching. This paper proposes a novel image registration method based on geometrical outlier removal. The purpose of this algorithm is to eliminate most outliers and preserve as much inliers as possible. We formulate the outlier elimination method into a mathematical model of optimization, the geometric relationship of feature points is the constraint, and derive a simple closed-form solution with linear time and linear space complexities. This algorithm is divided into three key steps. First two remote sensing images are registered by scale-invariant feature transform(SIFT) algorithm. The initial feature points are generated by this step. Then the mathematical model is built and the optimal solution is calculated based on the initial feature points. Last we compare two recent registration results based on the optimal solution, and determine if it is necessary to update the initial feature points and recalculate. The experiment results demonstrate the accuracy and robustness of the proposed algorithm.
Owing to significant geometric distortions and illumination differences, high precision and robust matching of multisource remote sensing image registration poses a challenge. This paper presents a new approach, called iterative scale invariant feature transform (ISIFT) with rectification (ISIFTR), to remote sensing image registration. Unlike traditional SIFT-based methods or modified SIFT-based methods, the ISIFTR includes rectification loops to obtain rectified parameters in an iterative manner. The SIFT-based registration results is updated by rectification loops iteratively and terminated by an automatic stopping rule. ISIFTR works in three stages. The first stage is used to capture consistency feature sets with maximum similarity followed by a second stage to compare the registration parameters between two successive iterations for updating and finally concluded by a third stage to terminate the algorithm. The experimental results demonstrate that ISIFTR performed better registration accuracy than SIFT without rectification. By comparing the iteration curve based on the four different similarity metric, the results illustrate that the RIRMI-based rectification obtains better results than other similarity metrics.
KEYWORDS: Target detection, Infrared radiation, Infrared imaging, Infrared detectors, Detection and tracking algorithms, Image segmentation, Infrared search and track, Signal to noise ratio
Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of infrared search and tracking (IRST) system. The images with small targets are usually of quite low signal-to-noise ratios, which makes the targets very difficult to be detected. To solve this problem, an effective infrared small target detection algorithm is presented in this paper. Firstly, a nested structure of the original pixel-wise image is constructed and the local structural discontinuity of each pixel is measured by a vector so-called local contrast vector (LCV). Each element of LCV describes the minimal difference between the central region and its neighboring regions, and the scale variety of regions results in the variety of elements. Then, a multi-dimensional image is generated with respect to LCV. After that, a confidence map for small target detection is reconstructed by signed normalization, that is, each pixel in the confidence map is generated by signed inner product of LCV. Finally, we segment the targets from the confidence map by utilizing an adaptive threshold. Extensive experimental evaluation results on a real test dataset demonstrate that our algorithm is superior to the state-of-the-art algorithms in detection performance.
Distinctive and robust local feature description is crucial for remote sensing application, such as image matching and image retrieval. A descriptor for multisource remote sensing image matching that is robust to significant geometric and illumination differences is presented. In the proposed method, a traditional scale-invariant feature transform algorithm is applied for local feature extraction and a feature descriptor, named robust center-symmetric local-ternary-pattern (CSLTP) based self-similarity descriptor, is constructed for each extracted feature point. The main idea of the proposed descriptor is a rotation invariance description strategy on local correlation surface. Unlike common distribution-based descriptors or geometric-based spatial pooling descriptors, the proposed descriptor uses rotation invariance statistically strategic for CSLTP description on a correlation surface, which is inherently rotation invariant and robust to complex intensity differences. Then, a bilateral matching strategy followed by a reliable outlier removal procedure in the geometric transformation model is implemented for feature matching and mismatch elimination. The proposed method is successfully applied for matching various multisource satellite images and the results demonstrate its robustness and discriminability compared to common local feature descriptors.
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