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The reliability of feature matching can decide the accuracy and robustness of the feature-based registration result. Aiming at the problem that the number of final feature matches preserved by many popular outlier removal methods is small, and the position accuracy of final feature matches is not high enough, we propose an accurate and robust image registration framework based on outlier removal and feature point adjustment in this paper. This framework increases the number and improves the position accuracy of inliers while eliminating most outliers. The increased number of inliers improves the robustness of image registration, and high accurate inliers improves the accuracy of image registration. Firstly, the initial feature matches are extracted by a commonly used feature-based registration method, such as the scale-invariant feature transform (SIFT)-based method. Then, outliers of the initial feature matches are eliminated by a frequency domain similarity measure, called PHase-based Structural SIMilarity (PH-SSIM) proposed in this paper. Considering the inherent error of the feature matches that still exist after the outlier elimination, a PH-SSIM-based feature point adjustment strategy is designed to fine-adjust the position of the preserved feature points in the reference image. Finally, the registration parameters are calculated by the fine-adjusted feature matches. The proposed framework has been evaluated by several remote sensing images with different resolution, grey-scale, texture, and scene, and compared with four state-of-the-art image registration methods. Experimental result demonstrates the high accuracy and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Owing to significant geometric distortions and illumination differences, high precision and robust matching of multisource remote sensing images is a difficult task. To solve this, mutual information (MI)-based methods have been a preferred choice, as MI represents a measure of statistical dependence between the two images. However, MI only considers original grey information and neglects spatial information in the calculation of the probability distribution. In this paper, a novel similarity metric based on rotationally invariant regional mutual information (RIRMI) is proposed. The RIRMI metric is constructed by combining MI with a regional information based on the statistical relationship between rotationally invariant centre-symmetric local binary patterns of the images. The similarity metric based on RIRMI considers not only the spatial information, but the effect of the local grey variations and rotation changes on computing probability density function as well. The proposed method is tested on various simulated remote sensing images (5-30 m GSD) and real remote sensing images (2-30 m GSD) which are taken at different sensors, spectral bands, and times. Results verify that RIRMI is more robust and accurate than the common MI-based registration method. [ABSTRACT FROM AUTHOR]

Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, theWoodbury identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors. [ABSTRACT FROM AUTHOR]

In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to their powerful reconstruction ability compared with traditional methods. However, actual probability distribution statistics hidden in the latent space are not discovered by exploiting the reconstruction error because the probability distribution of anomalies is not explicitly modeled. To address the issue, we propose a novel probability distribution representation detector (PDRD) that explores the intrinsic distribution of both the background and the anomalies for hyperspectral anomaly detection in this paper. First, we represent the hyperspectral data with multivariate Gaussian distributions from a probabilistic perspective. Then, we combine the local statistics with the obtained distributions to leverage the spatial information. Finally, the difference between the test pixel and the average expectation of the pixels in the Chebyshev neighborhood is measured by computing the modified Wasserstein distance to acquire the detection map. We conduct the experiments on three real data sets to evaluate the performance of our proposed method. The experimental results demonstrate the accuracy and efficiency of our proposed method compared to the state-of-the-art detection methods. [ABSTRACT FROM AUTHOR]

Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in Taiwan rely heavily on measured water depth data. To address these problems, this study proposes a largescale remote sensing inversion model of sand wave topography based on long short-term memory network machine learning. Using multi-angle sun glitter remote sensing to obtain sea surface roughness (SSR) information and by learning and training SSR and its corresponding water depth information, the sand wave topography of a largescale shallow sea sand wave region is extracted. The accuracy of the model is validated through its application to a 774 km2 area in the sand wave topography of the Taiwan Banks. The model obtains a root mean square error of 3.31–3.67 m, indicating that the method has good generalization capability and can achieve a largescale topographic understanding of shallow sand waves with some training on measured bathymetry data. Sand wave topography is widely present in tidal environments; our method has low requirements for ground data, with high application value. [ABSTRACT FROM AUTHOR]

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results. [ABSTRACT FROM AUTHOR]

Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of remote sensing. Conventional algorithms can fail in detecting small targets due to the low signal-to-noise ratios of the images. To solve this problem, an effective infrared small target detection algorithm inspired by random walks is presented in this paper. The novelty of our contribution involves the combination of the local contrast feature and the global uniqueness of the small targets. Firstly, the original pixel-wise image is transformed into an multi-dimensional image with respect to the local contrast measure. Secondly, a reconstructed seeds selection map (SSM) is generated based on the multi-dimensional image. Then, an adaptive seeds selection method is proposed to automatically select the foreground seeds potentially placed in the areas of the small targets in the SSM. After that, a confidence map is constructed using a modified random walks (MRW) algorithm to represent the global uniqueness of the small targets. 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 both target enhancement and detection performance. [ABSTRACT FROM AUTHOR]