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.
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.
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.
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.