Edge Preservation in Ikonos Multispectral and Panchromatic Imagery Pan-sharpening

International audienceIn Ikonos imagery, both multispectral (MS) and panchromatic (PAN) images are provided with different spatial and spectral resolutions. Multispectral classification detects object classes only according to the spectral property of the pixel. Panchromatic image segmentation enables the extraction of detailed objects, like road networks, that are useful in map updating in Geographical Information Systems (GIS), environmental inspection, transportation and urban planning, etc. Therefore, the fusion of a PAN image with MS images is a key issue in applications that require both high spatial and high spectral resolutions. The fused image provides higher classification accuracy. To extract, for example, urban road networks in pan-sharpened images, edge information from the PAN image is used to eliminate the misclassified objects. If the PAN image is not available, then an edge map is extracted from the pan-sharpened images, and therefore the quality of this map depends on the fusion process of PAN and MS images. In a pan-sharpening process, before fusing, the MS images are resampled to the same pixel sizes as the PAN images and this upsampling impacts subsequent processing. In this work, we demonstrate that the interpolation method, used to resample the MS images, is very important in preserving the edges in the pan-sharpened images

A New Evaluation Protocol for Image Pan-sharpening Methods

International audiencePan-sharpening consists in fusing the spatial and spectral characteristics of panchromatic and multispectral (MS) images to get synthesized MS images. When such a fusion technique is proposed, it is delicate and important to evaluate its results. Generally, to evaluate the pan-sharpening methods both spectrally and spatially, a variety of quality indexes are available. Although, spectral indexes play a more important role than spatial ones to assess the fusion methods, spatial quality is important too. In this paper, a new protocol is proposed to evaluate pan-sharpening methods. This evaluation, by considering both spectral and spatial indexes, facilitates, reduces and even avoids any visual analyses, and allows automatic classification when comparing fusion methods

A high-resolution index for vegetation extraction in IKONOS images

ISBN: 978-0-8194-8341-6 - WOSInternational audienceIn monitoring vegetation change and urban planning, the measure and the mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular approach to generate vegetation maps for remote sensing imagery. Unfortunately, the NDVI generates low resolution vegetation maps. Highresolution imagery, such as IKONOS imagery, can be used to overcome this weakness leading to better classification accuracy. Hence, it is important to derive a vegetation index providing the high-resolution data. Various scientific researchers have proposed methods based on high-resolution vegetation indices. These methods use image fusion to generate high-resolution vegetation maps. IKONOS produces high-resolution panchromatic (Pan) images and low-resolution multispectral (MS) images. Generally, for the image fusion purpose, the conventional linear interpolation bicubic scheme is used to resize the low-resolution images. This scheme fails around edges and consequently produces blurred edges and annoying artefacts in interpolated images. This study presents a new index that provides high-resolution vegetation maps for IKONOS imagery. This vegetation index (HRNDVI: High Resolution NDVI) is based on a new derived formula including the high-resolution information. We use an artefact free image interpolation method to upsample the MS images so that they have the same size as that of the Pan images. The HRNDVI is then computed by using the resampled MS and the Pan images. The proposed vegetation index takes the advantage of the high spatial resolution information of Pan images to generate artefact free vegetation maps. Visual analysis demonstrates that this index is promising and performs well in vegetation extraction and visualisation

Skuteczna metoda rozpakowywania obrazu dookólnego

Omnidirectional cameras are widely used almost everywhere especially in robotics and security. These kinds of cameras provide circular images that can be difficult to be interpreted by humans. Hence it must be transformed to be understandable by humans or to be treated with usual systems. This transformation is called unwrapping. The unwrapping may be time consuming and can decrease the performance of the real-time systems. Besides, the unwrapping can affect the quality of obtained images. To overcome these problems, we present an efficient parallel omnidirectional image unwrapping approach based on image partitioning. Experimental results indicate that our unwrapping approach has fast processing and gives better quality of unwrapped panoramic images.Kamery dookólne są szeroko stosowane niemal wszędzie, zwłaszcza w robotyce i bezpieczeństwie. Tego rodzaju kamery dostarczają okrągłe obrazy, które mogą być trudne do zinterpretowania przez ludzi. W związku z tym muszą one zostać przekształcone, aby były zrozumiałe dla ludzi lub mogły być przetwarzane przez zwykłe systemy. Transformacja ta nazywana jest rozpakowywaniem. Rozpakowywanie może być czasochłonne i może obniżyć wydajność systemów czasu rzeczywistego. Ponadto, rozpakowywanie może wpływać na jakość uzyskanych obrazów. Aby przezwyciężyć te problemy, przedstawiamy wydajne równoległe podejście do rozpakowywania obrazów dookólnych oparte na dzieleniu obrazów. Wyniki eksperymentów wskazują, że nasze podejście do rozpakowywania zapewnia szybkie przetwarzanie i lepszą jakość rozpakowanych obrazów panoramicznych

A Feature Point Based Image Registration Using Genetic Algorithms

Image registration has been widely applied in many fields such as remote sensing, medical image analysis, cartography, computer vision and pattern recognition. The key of image registration is to find the proper transformation of one image to another image so that each point of one image is spatially aligned with its corresponding point of the other. In this paper, we present a rigid feature point based image registration method integrating two techniques. The first is one in which we propose to extract the feature points by using efficiency of the multi-resolution representation data of the nonsubsampled contourlet transform. The second technique exploits the robustness of Genetic algorithms as an optimization method to find the best transformation parameters. The results show the effectiveness of this approach for registering the magnetic resonance images

A Rigid Image Registration Based on the Nonsubsampled Contourlet Transform and Genetic Algorithms

Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise

Design of orthogonal filter banks using a multi-objective genetic algorithm for a speech coding scheme

In this work, we propose an optimization scheme based on a multi-objective Genetic Algorithm (GA) for the design of orthogonal filter banks for speech compression. A parameterization is adopted to assure that the resulting filter banks satisfy perfect reconstruction and have at least two vanishing moments. We search for a parameter set that optimizes the coding gain and the frequency selectivity. As the objectives are conflicting, we investigate the solution that realizes the best compromise between the objectives criteria using the Non-dominated Sorting Genetic Algorithm (NSGAIII). Experimental results have shown that the optimized filter banks provide a significant gain in coding performances when comparing with the Daubechies orthogonal filter banks for test speech signals

Satellite image encryption using 2D standard map and advanced encryption standard with scrambling

In today’s world, the need for higher levels of security in storing and transferring data has become a key concern. It is essential to safeguard data from any potential information leaks to prevent threats that may compromise data confidentiality. Therefore, to protect critical and confidential satellite imagery, this paper proposes a novel encryption method based on the combination of image bands scrambling with chaos and the advanced encryption standard (AES). The proposed approach aims to enhance the security of satellite imagery while maintaining efficiency and robustness against various attacks. It possesses several appealing technical characteristics, notably a high level of security, a large key space, and resilience to single event upsets (SEUs) and transmission errors. To evaluate the performance of the proposed encryption technique, extensive experiments have been conducted by considering factors such as security level, resistance to SEUs, and computational efficiency. Our results demonstrate that the proposed method achieves a high level of security and a large key space, ensuring the confidentiality and integrity of satellite imagery data. Furthermore, the method exhibits resilience against SEUs and transmission errors, and offers efficient processing, making it suitable for real-world applications

An Improved Image Encryption Algorithm Based on Cyclic Rotations and Multiple Chaotic Sequences: Application to Satellite Images

In this paper, a new satellite image encryption algorithm based on the combination of multiple chaotic systems and a random cyclic rotation technique is proposed. Our contribution consists in implementing three different chaotic maps (logistic, sine, and standard) combined to improve the security of satellite images. Besides enhancing the encryption, the proposed algorithm also focuses on advanced efficiency of the ciphered images. Compared with classical encryption schemes based on multiple chaotic maps and the Rubik's cube rotation, our approach has not only the same merits of chaos systems like high sensitivity to initial values, unpredictability, and pseudo-randomness, but also other advantages like a higher number of permutations, better performances in Peak Signal to Noise Ratio (PSNR) and a Maximum Deviation (MD)