Mohamed El Haziti

An Efficient Approach for Filling Gaps in Landsat 7 Satellite Images

Landsat 7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery presents an important data source for many applications related to remote sensing. However, the scan line corrector (SLC) failure has seriously limited the scientific applications of ETM+ data since SLC failed permanently on May 31, 2003, resulting in about 22% of the image data missing in each scene. In this letter, we propose to apply a new method to fill the missing information (gap) in the satellite image without any spatial or spectral constraint applied on the neighborhood of missing pixels. The method can also simultaneously remove various types of noise, including Gaussian noise and impulse noise, affected in the acquisition process. This proposed concept is formulated as a consecutive multilevel Otsu and Two-Threshold Binary Decomposition to detect the boundary of the damaged region (gaps) and then reconstruct the edge of the damaged region based on boundary restoration. In the final step, we applied the well-known matrix completion as a low-rank approximation problem guided by boundary reconstructed. We achieve better approximation of the rank by minimizing the truncated nuclear norm with accelerated proximal gradient line, a method for convex optimization problems. The results show the capability of our approach to predict the missing values accurately in terms of quality, time, and without the need of outside information. This letter also shows encouraging results on both synthetic and real visual data sets.

Wireless Sensor Networks in Biomedical: Wireless Body Area Networks

A wireless sensor networks can be used in several applications to monitor certain parameters like smart homes, agriculture field monitoring and healt care monitoring. In this work, we propose a routing protocol for wireless body area network, to transfer data with minimum energy consumption and longer network life time, we use a multi-hop topology and we evaluate the performance of our approach by simulations.

Hybrid blind robust image watermarking technique based on DFT-DCT and Arnold transform

In this paper, a robust blind image watermarking method is proposed for copyright protection of digital images. This hybrid method relies on combining two well-known transforms that are the discrete Fourier transform (DFT) and the discrete cosine transform (DCT). The motivation behind this combination is to enhance the imperceptibility and the robustness. The imperceptibility requirement is achieved by using magnitudes of DFT coefficients while the robustness improvement is ensured by applying DCT to the DFT coefficients magnitude. The watermark is embedded by modifying the coefficients of the middle band of the DCT using a secret key. The security of the proposed method is enhanced by applying Arnold transform (AT) to the watermark before embedding. Experiments were conducted on natural and textured images. Results show that, compared with state-of-the-art methods, the proposed method is robust to a wide range of attacks while preserving high imperceptibility.

A New Generation of Wireless Sensors Networks: Wireless Body Area Networks

A wireless Sensor networks consists basically of a group of nodes that communicate with each other through a wireless transmission and does not need any existing infrastructure. These wireless sensor networks can be used in several applications to monitor many parameters in different area. The main objective in this communication is how to transfer data in the network with minimum energy consumption and keeping the quality of information send in the network. In this work, we propose a routing protocol for wireless body area network, for that, we use a multi hop topology to transfer data with minimum energy consumption and longer network life time. The simulation show that our protocol has not only the best performance but also the best energy efficiency.

A robust blind 3-D mesh watermarking based on wavelet transform for copyright protection

Nowadays, three-dimensional meshes have been extensively used in several applications such as, industrial, medical, computer-aided design (CAD) and entertainment due to the processing capability improvement of computers and the development of the network infrastructure. Unfortunately, like digital images and videos, 3-D meshes can be easily modified, duplicated and redistributed by unauthorized users. Digital watermarking came up while trying to solve this problem. In this paper, we propose a blind robust watermarking scheme for three-dimensional semiregular meshes for Copyright protection. The watermark is embedded by modifying the norm of the wavelet coefficient vectors associated with the lowest resolution level using the edge normal norms as synchronizing primitives. The experimental results show that in comparison with alternative 3-D mesh watermarking approaches, the proposed method can resist to a wide range of common attacks, such as similarity transformations including translation, rotation, uniform scaling and their combination, noise addition, Laplacian smoothing, quantization, while preserving high imperceptibility.

Noisy Satellite Image Segmentation Using Statistical Features

Satellite image segmentation is a principal task in many applications of remote sensing such as natural disaster monitoring and residential area detection and especially for Smart cities, which make demands on Satellite image analysis systems. This type of image (satellite image) is rich and various in content however it suffers from noise that affects the image in the acquisition. The most of methods retrieve the textural features from various methods but they do not produce an exact descriptor features from the image and they do not consider the effect of noise. Therefore, there is a requirement of an effective and efficient method for features extraction from the noisy image. This paper presents an approach for satellite image segmentation that automatically segments image using a supervised learning algorithm into urban and non-urban area. The entire image is divided into blocks where fixed size sub-image blocks are adopted as sub-units. We have proposed a statistical feature including local feature computed by using the probability distribution of the phase congruency computed on each block. The results are provided and demonstrate the good detection of urban area with high accuracy in absence of noise but a low accuracy when noise is added which yields as to present a novel features based on higher order spectra known by their robustness against noise.

A blind robust image watermarking approach exploiting the DFT magnitude

Due to the current progress in Internet, digital contents (video, audio and images) are widely used. Distribution of multimedia contents is now faster and it allows for easy unauthorized reproduction of information. Digital watermarking came up while trying to solve this problem. Its main idea is to embed a watermark into a host digital content without affecting its quality. Moreover, watermarking can be used in several applications such as authentication, copy control, indexation, Copyright protection, etc. In this paper, we propose a blind robust image watermarking approach as a solution to the problem of copyright protection of digital images. The underlying concept of our method is to apply a discrete cosine transform (DCT) to the magnitude resulting from a discrete Fourier transform (DFT) applied to the original image. Then, the watermark is embedded by modifying the coefficients of the DCT using a secret key to increase security. Experimental results show the robustness of the proposed technique to a wide range of common attacks, e.g., Low-Pass Gaussian Filtering, JPEG compression, Gaussian noise, salt & pepper noise, Gaussian Smoothing and Histogram equalization. The proposed method achieves a Peak signal-to-noise-ration (PSNR) value greater than 66 (dB) and ensures a perfect watermark extraction.