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Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.

As a first step for image processing operations, detection of corners is a vital procedure where it can be applied for many applications as feature matching, image registration, image mosaicking, image fusion, and change detection. Image registration can be defined as process of getting the misalignment of pixel's position between two or more images. In this paper, a modified corner detector named Synthetic Aperture Radar-Phase Congruency Harris (SAR-PCH) based on a combination between both phase congruency, named later PC, and Harris corner detector is proposed where PC image can supply fundamental and significative features although the complex changes of intensities. Also, the proposed approach overcomes the Harris limitation concerning the noise since the Harris is more sensitive to the noise. The performance was similitude with Shi-Tomasi, FAST, and Harris corner detectors where experiments are conducted first with simulated images and second with real ones. Mean square error (MSE) and peak signal-to-noise ratio (PSNR) are used for the simile. Experimental results, carried out in a standard computer, verify its effectiveness where it utilizes the privileges of image constitutional depicting, allowing extraction of the most powerful key points since it preserves robustness of co-registration process using image frequency properties which are not variant to illumination. Reasonable results compared to the state of art method as Shi-Tomasi, FAST, and Harris algorithms were achieved on the expense of high computational processing time that can be recovered using hardware having high capabilities.

Multi-sensor remote sensing data can significantly improve the interpretation and usage of large volume data sources. A combination of satellite Synthetic Aperture Radar (SAR) data and optical sensors enables the use of complementary features of the same image. In this paper, SAR data is injected into optical image using a combining fusion method based on the integration of wavelet Transform and IHS (Intensity, Hue, and Saturation) transform. Not only to preserve the spectral information of the original (MS) image, but also to maintain the spatial content of the high-resolution SAR image. Two data sets are used to evaluate the proposed fusion algorithm: one of them is Pleiades, Turkey and the other one is Boulder, Colorado, USA. The different fused outputs are compared using different image quality indices. Visual and statistical assessment of the fused outputs displays that the proposed approach has an effective translation from SAR to the optical image. Hence, enhances the SAR image interpretability.

SAR imaging needs precise registration process to get recompensation or setoff between two or more images for a certain area. Generally, operational goal of registration process is a geometrical transformation for the input leading to geometrically agreement for input images. Coregistration step can be used in a lot of fields as change detection, mosaicking, super-resolution images creation, … etc. Registration process can be classified into two main classes: Classical or Automatic methods. For the classical approach, the anchor points are picked out manually and then the transformation modeling is performed, that leads to more time consuming and low output accuracy. Automatic detection of these anchor points aims to recover the functionality of non-automated processes. Registration process should recover many problems such as illumination changes, variations of intensity, Different sensors, noise, … etc. Its applications are mainly dependent on errors, such as multi temporal; multi view; or multi modal, occurred during capturing process. Feature detection, as a step of image registration process, obtains a group of anchors, that are stable and informativeness, or patches under varying conditions. Also, it is critical for the detector to have robustness against viewpoint changes, brightness changes, and other deformations. Detailed discussion and investigation are paid, in this manuscript, for the common corner detectors helping to be familiar with the various applied techniques for feature detection. Our goal is to examine a combination hypothesis of these methods into an advanced selective one.

Chaos theory describes the behavior of certain nonlinear deterministic systems. These systems with evolving state time-dependence variation, may exhibit dynamics that is highly sensitive to initial conditions. Generally, the chaotic generators are classified into two main classes; continuous and discrete time generators. Continuous time such as systems based on Chua’s circuit[1] or the Lorenz[2] and Rossler systems[3], while discrete time systems such as Henon map[4]. Many methods that use analog circuits have been proposed in the field of chaotic communication systems[5-10]. Because recovery characteristics are sensitive to parameter mismatch between the receiver and the transmitter, one deficiency of these systems is that it must have both the transmitter and the receiver with very high component accuracy, to ensure correct information recovery. However, in practical situations, it is difficult to build both the transmitter and the receiver with very high component accuracy, since the component values are affected by aging, temperature…etc. Therefore, the analog implementation seems very difficult. Software implementations provide powerful computing tools in which complex numerical simulations of nonlinear phenomena are possible. They offer several advantages such as precision and ease of use to change the parameter values. Despite these privileges, applications such as spread spectrum and cryptography systems need high level of security; this is achieved by using hardware implementations instead of software implementations. In this paper, discrete time generators representing a modification of analog generators will be described. The goal here is to overcome the problems which face the analog circuits.

Registration or matching process aims to find the misalignment between two or more images concerning the same area to detect the values of the mapping matrix in order to transform interest points in one image to its correspondence in the others. This paper presents a dynamic approach aiming to improve the performance of the registration process for synthetic aperture radar (SAR) images. First, the noise resulting from the capturing process is reduced by using a smoothing filter based on kernel-gaussian to reduce the amplification of noise. Then; a combination of two area- based matching (ABM) methods is used. The first method is carried out using Crosscorrelation approach, acting as coarse registration step. The second method is achieved by using regular step gradient descent (RSGD) optimizer, acting as fine registration step. Evaluation of the performance concerning the proposed manner is achieved by comparing to the state-of-the art detectors as Harris, Shi-Tomasi, and Features from Accelerated Segment Test (FAST) detectors. Metric factors to achieve the comparison are mean square error (MSE) and peak signal-to-noise ratio (PSNR) between the input images. Results demonstrate a highly performance for the proposed method compared to the others where it has a high robustness and minimizes the noise of the input image.

Image registration is a process to find the offset or misalignment between two or more images for a certain area to determine the required geometrical transformation that aligns points in one image with its corresponding in the other one. Generally, the operational goal of the registration process is a geometrical transformation for the input leading to geometrically agreement for input images, so that the matched pixels in the input images refer to the same region of the captured area. So, image registration can be applied in many applications such as change detection, mosaicking, creating super-resolution images etc. Registration process is divided into two categories: (1) Traditional methods and (2) Automated methods. For the traditional methods, the anchor, control, points are selected manually and applying the transformation model leading to time consuming and low accuracy. So, automatically detection of these points helps to recover the performance of manual selection. Registration process deals with many problems such as illumination changes, intensity variations, Different sensors, noise etc. So, its applications are mainly dependent on errors (multi temporal, multi view, or multi modal) occurred during capturing process. Feature detection, as a step of image registration process, aims to find a set of stable (invariant) distinctive key points or regions under varying conditions. Also, it is critical for the detector to be robust to changes in viewpoint, brightness, and other distortions. The goal of current paper is discussion and exhibition of the common corner detectors helping to be familiar with the various applied techniques for feature detection.

The target of high resolution digital terrain elevation sequence is to obtain 3-D representation of a terrain's surface. In this work we used interferometric technique which is based on the analysis of the phase difference between the signals received at two antenna position. Under suitable conditions, the resulting DEM of this method is locally the most precise one. This work is concerned with the exploitation of radar SLC images of the Las Vega area acquired on 24 May and 25 May 1994, the first is taken as the master image and the other as slave. We have developed an interferometric process beginning with the implementation of a co-registration algorithm for the images. Then we developed the flatearth phase elimination from the interferogram. We also developed a filter in order to minimiz the existing noise. At the end remove the effect of under sampling to get a good result in the final step of the process which is the unwrapping step.

The wide spread of WLAN and the popularity of mobile devices increase the frequency of data transmission among mobile users. However, most of the data encryption technology is location-independent. Indeed, an encrypted data can be decrypted anywhere. The encryption technology cannot restrict the location of data decryption. Therefore, our objective is to add a layer of security to the network without breaking the network rules and to decrease network traffic. These aims can be achieved by two ways. First, almost all the fractionally encrypted messages sent by senders are decrypted by the receivers by applying the inverse fractional Fourier transform in the same order of rotations. Second, a decrease inthe message queuing delay occurs due to the no need for encryption technique as it was achieved by the fractional Fourier transform. In order to meet the demand of mobile users in the future, a location-dependent approach is proposed in this paper. Firstly a target latitude/longitude coordinate must be determined. The coordinate is incorporated with a random key for data encryption by fractional Fourier transform. The receiver can only decrypt the ciphertext when the coordinate acquired from GPS receiver is matched with the target coordinate, and the exact key for fractional transformation is applied.

Image registration is a fundamental image processing task to match and align physically two images which could have been imaged by different sensors, view angles and or at different times. This paper presents a new algorithm for image registration based on a combination between cross correlation function and feature-based methods .In the proposed algorithm, the ground control points are extracted from input images using the Harris corner detector, and the correspondence between the points extracted from the different images is established using RANdom SAmple Consensus (RANSAC) method with. The image coregistration in this paper is performed by three ways: It is first performed by the proposed algorithm then it is performed by feature-based methods and finally the results are compared with those achieved using Gamma design software. The method is also evaluated by using the coherence map. The results showed that the combination between cross correlation function and feature-based methods almost give us the same results of traditional method but with low processing time., which is our aim to prove and validate. (4 pages)