Satellite image fusion using Fast Discrete Curvelet Transforms

Remote sensing image fusion using the curvelet transform

Many intermodality models (IMMs) have been established to define how panchromatic (Pan) high-frequency details should be injected into the multispectral (MS) bands in pan-sharpening. In this paper, IMMs are categorized into two types, namely regression coefficient (RC) models and intensity modulation (IM) models. The RC is simply the ratio of the local covariance of x and y to the local variance of x, where x and y are the approximation of the Pan image at the MS scale and the MS image, respectively. To compare these two IMM types, an ideal IMM representing the proper amount of high-frequency details which should be injected is formulated. This ideal IMM is exactly the same formulation as the RC model except that x and y are high-resolution Pan and MS images, respectively. Therefore, compared to the ideal IMM, the RC model is influenced by the scale effect since the local variance and covariance change as functions of scale. The IM model, represented by the model in the high-pass filtering (HPM) method, may introduce improper injections which are affected by landscape complexity, spectral response function, atmospheric conditions, and offset values for converting radiance into digital number (DN). Using the information from the high-resolution Pan image, an improved HPM method is proposed for shadow regions, which are quite common in very high-resolution images. Experimental results using a WorldView-2 image covering an urban area and a QuickBird image covering a rural area confirmed our analytical conclusions on different IMMs and indicate that the improved HPM model performs better than the original HPM.

Introduction We previously reported a method to estimate flow limitation (FL, range 0-~1) from the airflow signal recorded within a polysomnogram. This method utilised high quality nasal pressure signals, without high-pass (HP) filtering. However, many clinical recording systems implement HP filtering on airflow signals. We hypothesised that FL estimates would be adversely affected when airflow signals have HP filtering present. Methods Within our existing framework for estimating FL, we manipulated the airflow signal by applying digital HP filtering (Butterworth, 4th order) at commonly used cutoff frequencies (0.005, 0.01, 0.03, 0.1Hz). At each HP filter cutoff, performance was assessed at the breath-level (pooled, all patients) and patient-level (median, sleep only) using coefficient of determination (R²) and root mean squared error (RMSE) comparing FL estimates with HP filtering against reference data without HP filtering. Results In N=96 individuals, R² decreased while RMSE increased as HP filter cutoff increased. For HP filter cutoffs at 0.005, 0.01, 0.03 and 0.1Hz, breath-level R² was 0.923, 0.879, 0.740, and 0.483; while RMSE was 0.062, 0.079, 0.129, and 0.219, respectively. Similarly, patient-level R² was 0.994, 0.977, 0.843, and 0.738; while RMSE was 0.015, 0.030, 0.100, and 0.170. Discussion Increasing HP cutoff frequency resulted in a reduction of consistency of FL estimates when compared to those made using an unfiltered airflow signal. Further work including reference to ‘gold standard’ is required, however, in line with current AASM recommendation (“Direct current (DC) or ≤0.03Hz”), this study suggests HP filter cutoff frequency for nasal pressure should be minimised, preferably avoided.

Image fusion is a formal framework which is expressed as means and tools for the alliance of multisensor, multitemporal, and multiresolution data. Multisource data vary in spectral, spatial and temporal resolutions necessitating advanced analytical or numerical techniques for enhanced interpretation capabilities. This paper reviews seven pixel based image fusion techniques -– intensity-hue-saturation, brovey, high pass filter (HPF), high pass modulation (HPM), principal component analysis, fourier transform and correspondence analysis. Validation of these techniques on IKONOS data (Panchromatic band at 1 m spatial resolution and Multispectral 4 bands at 4 m spatial resolution) reveal that HPF and HPM methods synthesise the images closest to those the corresponding multisensors would observe at the high resolution level.

Video steganography mainly deals with secret data transmission in a carrier video file without being visually noticeable by intruders. Video steganography is preferred over image steganography because a video carries more space in comparison to an image. The main concept of information hiding consists of a cover media, which is a greyscale or a color video, a secret data, which is an image or text, and a stego key. Here a secure video steganography method has been proposed which uses Curvelet Transform for secret data embedding, Elliptic Curve Cryptography for stego key encryption and a threshold algorithm for the determination of the amount of secret data to be encoded per frame. A video is a collection of various frames. The frames are selected randomly from the cover video and the frame number of the respective frames has been indexed in the stego key to find the secret data embedding location. Here, the selection of frames in a sequential manner has been avoided to improve security. For enhanced security, the stego key is also encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES). Fast Discrete Curvelet Transform (FDCT) has been applied to the frames of the cover video and the curvelet coefficients have been modified to obscure the secret data to produce the stego video.

Ground-roll is an interferential wave existing widely in seismic data, and it is an important step in seismic data processing to suppress ground-roll. Wavelet Transform lacks of detecting the singularities of the signal with straight line or curve, and the method can separate the different waves at the same time because of Curvelet transform simultaneously has the characteristics of frequency and direction. The experiment results show that the Curvelet transform has better effects in ground-roll suppression.

Digital watermarking technology means that the hidden information is embedded in digital multimedia data by way of signal processing. In this paper, we firstly analyze the Fast Discrete Curvelet Transforms (FDCT) and Discrete Wavelet Transforms (DWT) which are usually used in image processing and figure out that DWT can not approximate optimally the image which contains the line singularity. And then a new encrypted embedded algorithm is presented based on FDCT. Based on bit-plane of gray image, the algorithm makes use of the advantage of FDCT and the watermark is encrypted by Chaotic Map and Arnold Transforms (AT) which is imbedded into the proper coefficient of FDCT. The result of our experiment shows that this algorithm is robust and transparent compared with DWT.

This article presents an efficient method for power quality events (PQEs) detection and classification based on Curvelet transform (CT) and optimized extreme learning machine (OELM). Initially, various PQEs signal data are extracted even under noisy and simultaneously occurred multi-event conditions to reflect the real-time conditions. Relevant features of all these extracted signals are computed by using a multi-resolution and multidirectional fast discrete CT (FDCT) approach. These features are used to classify the events accurately by the proposed OELM. In this classification strategy, a modified differential evolution (MDE) is introduced to optimize the conventional ELM to enhance its precision rate. The proposed novel approach is tested and analyzed under various noisy conditions with 20 dB, 30 dB, and 50 dB with single and multi PQEs conditions. Comparative results with other recently proposed approaches by various authors reveal that the proposed CT- and OELM-based classifier titled CT-OELM can be considered as a competitive choice for implementing in the real-time monitoring system.

There are many image fusion methods that can be used to produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolution multispectral images. Starting from the physical principle of image formation, this paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods. Using the GIF method, it is shown that the pixel values of the high-resolution multispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level. Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the a/spl grave/ trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method. The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set. An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level.

The feature extraction plays a very important role in iris recognition. Recent researches on multiscale analysis provide good opportunity to extract more accurate information for iris recognition. In this work, a new directional iris texture features based on 2-D Fast Discrete Curvelet Transform (FDCT) is proposed. The proposed approach divides the normalized iris image into six sub-images and the curvelet transform is applied independently on each sub-image. The anisotropic feature vector for each sub-image is derived using the directional energies of the curvelet coefficients. These six feature vectors are combined to create the resultant feature vector. During recognition, the nearest neighbor classifier based on Euclidean distance has been used for authentication. The effectiveness of the proposed approach has been tested on two different databases namely UBIRIS and MMU1. Experimental results show the superiority of the proposed approach.

The electrical excitation flux-switching (EEFS) machine, which exhibits advantages including high reliability, good flux regulation capability, bipolar flux linkage and sinusoidal back-EMF, is suitable for use as a generator in DC power systems. In this paper, a direct torque linear control (DTLC) scheme is investigated for voltage regulation of the EEFS generator. This scheme can achieve direct and smooth control of the instantaneous torque by establishing the strict linear relationship between the torque and the sine value of the torque angle, thus will improve the system steady-state and dynamic performance. However, the stator flux estimation accuracy will affect torque control quality. Hence, a simple and practical stator flux estimation method, which only contains a second-order low pass (LP) filter, a high pass (HP) filter and a simple coordinate transformation module, is proposed for both the DTLC and the direct torque control (DTC) schemes of the EEFS generator. In this estimation method, the HP filter can completely filter out the DC drift in the back-EMF, and the leading phase produced by the second-order LP filter and HP filter can be compensated by the coordinate transformation module. Hence, at any synchronous angular frequency, the amplitude-frequency and phase-frequency characteristics of the new estimation method are same as those of the pure integrator, while DC gain of this method is zero. The DTLC scheme of the EEFS generator using this estimation method exhibits good steady-state and dynamic performance, which is verified by experimental results.

Sharpening enhancement of Computed Tomography (CT) images using Hyperbolic Secant Square filter

To improve the quality of the fused image, we propose a remote sensing image fusion method based on sparse representation. In the method, first, the source images are divided into patches and each patch is represented with sparse coefficients using an overcomplete dictionary. Second, the larger value of sparse coefficients of panchromatic (Pan) image is set to 0. Third, Then the coefficients of panchromatic (Pan) and multispectral (MS) image are combined with the linear weighted averaging fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. The proposed method is compared with intensity-hue-saturation (IHS), Brovey transform (Brovey), discrete wavelet transform (DWT), principal component analysis (PCA) and fast discrete curvelet transform (FDCT) methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.

To improve the quality of the fused image, we propose a remote sensing image fusion method based on sparse representation. In the method, first, the source images are divided into patches and each patch is represented with sparse coefficients using an overcomplete dictionary. Second, the larger value of sparse coefficients of panchromatic (Pan) image is set to 0. Third, Then the coefficients of panchromatic (Pan) and multispectral (MS) image are combined with the linear weighted averaging fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. The proposed method is compared with intensity-hue-saturation (IHS), Brovey transform (Brovey), discrete wavelet transform (DWT), principal component analysis (PCA) and fast discrete curvelet transform (FDCT) methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.

Effects of EMG processing on biomechanical models of muscle joint systems: Sensitivity of trunk muscle moments, spinal forces, and stability