1,720,975 research outputs found
Lossy compression of hyperspectral images using shearlet transform and 3D SPECK
No full textAbstract: In this paper, a new lossy compression method for hyperspectral images (HSI) is introduced. HSI are considered as a 3D dataset with two dimensions in the spatial and one dimension in the spectral domain. In the proposed method, first 3D multidirectional anisotropic shearlet transform is applied to the HSI. Because, unlike traditional wavelets, shearlets are theoretically optimal in representing images with edges and other geometrical features. Second, soft thresholding method is applied to the shearlet transform coefficients and finally the modi fied coefficients are encoded using Three Dimensional-Set Partitioned Embedded bloCK (3D SPECK). Our simulation results show that the proposed method, in comparison with well-known approaches such as 3D SPECK (using 3D wavelet) and combined PCA and JPEG2000 algorithms, provides a higher SNR (signal to noise ratio) for any given compression ratio (CR). It is noteworthy to mention that the superiority of proposed method method is distinguishable as the value of CR grows. In addition, the effect of proposed method on the spectral unmixing analysis is also evaluated
SAR image denoising using homomorphic and shearlet transforms
No full textAbstract: Recently, denoising of Synthetic Aperture Radar (SAR) images has gained particular attention. SAR image is usually affected by speckle noise. In this paper a new method for speckle noise reduction of SAR images using shearlet transform (ST) is introduced. ST could significantly remove the Gaussian noise therefore in the proposed method first, noisy images are converted to a domain which type of noise is Gaussian using homomorphic transform (HT). Second, 2D shearlet is applied to the data. Third, the hard thresholding is used in order to denoise the shearlet coefficients. Finally reconstructed denoised images are obtained by applying the inverse shearlet and homomorphic transforms. The proposed method (ST-HT) is compared with state of art denoising algorithms on SAR images. Obtained results show the superiority of the proposed approach
The effect of denoising on superresolution of hyperspectral imaging
No full textAbstract: Hyperspectral Images (HSI) are usually affected by different type of noises such as Gaussian and non-Gaussian. The existing noise can directly affect the classification, unmixing and superresolution analyses. In this paper, the effect of denoising on superresolution of HSI is investigated. First a denoising method based on shearlet transform is applied to the low-resolution HSI in order to reduce the effect of noise, then the superresolution method based on Bayesian sparse representation is used. The proposed method is applied to real HSI dataset. The obtained results of the proposed method in comparison with some of the state-of-the-art superresolution methods show that the proposed method significantly increases the spatial resolution and decreases the noise effects efficiently
Compression and noise reduction of hyperspectral images using non-negative tensor decomposition and compressed sensing
No full textAbstract: Hyperspectral images ( HSI) are usually volumetric and require alot of space and time for archiving and transmitting. In this research, a new lossy compression method for HSI is introduced based on non-negative Tucker decomposition ( NTD). This method consider HSI as a 3D dataset: two spatial dimensions and one spectral dimension. The NTD algorithm decomposes the original data into a smaller 3D dataset ( core tensor) and three matrices. In the proposed method, the Block Coordinate Descent ( BCD) method is used to find the optimal decomposition, which is initialized by using Compressed Sensing ( CS). The obtained optimal core tensor and matrices are coded by applying arithmetic coding and finally the compressed dataset is transmitted. The proposed method is applied to the real dataset, simulation results show that in comparison with well-known lossy compression methods such as 3D SPECK and PCA+JPEG2000, the proposed method achieves the highest signal to noise ratio ( SNR) at any desired compression ratio ( CR) while noise reduction is simultaneously acquired
Image denoising using generalised Cauchy filter
No full textAbstract: In many image processing analysis, it is important to significantly reduce the noise level. This study aims at introducing an efficient method for this purpose based on generalised Cauchy (GC) distribution. Therefore, some characteristics of GC distribution is considered. In particular, the characteristic function of a GC distribution is derived by using the theory of positive definite densities and utilising the density of a GC random variable as the characteristic function of a convolution of two generalised non-symmetric Linnik variables. Further, GC distribution is considered as a filter and in the proposed method for image noise reduction the optimal parameters of GC filter is defined by using the particle swarm optimisation. The proposed method is applied to different types of noisy images and the obtained results are compared with four state-of-the-art denoising algorithms. Experimental results confirm that their method could significantly reduce the noise effect
Lossy compression of hyperspectral images optimizing spectral unmixing
No full textAbstract: In this paper, we present a new hyperspectral image lossy compression method that aims to optimally compress in both spatial and spectral domains and simultaneously considers linear spectral unmixing as a target. To achieve this, a non-negative tucker decomposition is applied. This algorithm has three flexible dimension parameters. We propose an approach that, for any desired compression ratio (CR), chooses the optimal parameters by minimizing the root mean square error (RMSE) between the abundance matrices of the original and compressed datasets using fully constrained least square spectral unmixing. The resulting optimization problem is solved by a Particle Swarm Optimization algorithm. Our simulation results show that the proposed method, in comparison with well-known lossy compression methods such as 3D-SPECK and combined PCA+JPEG2000 algorithms, provides a lower RMSE and higher signal to noise ratio (SNR) for any given CR. It is noteworthy to mention that the superiority of our method becomes more apparent as the value of CR grows
Band-specific shearlet-based hyperspectral image noise reduction
No full textAbstract: Hyperspectral images (HSIs) can be very noisy, and the amount of noise may differ from band to band. While some spectral bands may be dominated by low signal-independent noise levels, others have mixed noise levels, which may include high levels of Gaussian, Poisson, and Spike noises. When a denoising algorithm is globally applied to the whole data set, it usually affects the low-noise bands adversely. Therefore, it is better to use different criteria for denoising different bands. In this paper, we propose a new denoising strategy to do so. The method is based on a 2-D nonsubsampled shearlet transform, applied to each spectral band of the HSI. We propose an effective method to distinguish between bands with low levels of Gaussian noise (LGN bands) and bands with mixed noise (MN bands) based on spectral correlation. LGN bands are denoised using a thresholding technique on the shearlet coefficients. On the MN bands, a local noise reduction method is applied, in which the detail shearlet coefficients of adjacent LGN bands are employed. This targeted approach is prone to reduce spectral distortions during denoising compared with global denoising methods. This advantage is shown in experiments where the proposed method is compared with state-of-the-art denoising methods on synthetic and real hyperspectral data sets. To assess the effect of denoising, classification and spectral unmixing tasks are applied to the denoised data. Obtained results show the superiority of the proposed approach
Hyperspectral image compression optimized for spectral unmixing
No full textAbstract: In this paper, we present a new lossy compression method for hyperspectral images that aims to optimally compress in both spatial and spectral domains and simultaneously minimizes the effect of the compression on linear spectral unmixing performance. To achieve this, a nonnegative Tucker decomposition is applied. This decomposition is a function of three dimension parameters. By employing a link between this decomposition and the linear spectral mixing model, an optimization problem is defined to find the optimal parameters by minimizing the root-mean-square error between the abundance matrices of the original and reconstructed data sets. The resulting optimization problem is solved by a particle swarm optimization algorithm. An approximate method for fast estimation of the free parameters is introduced as well. Our simulation results show that, in comparison with well-known state-of-the-art lossy compression methods, an improved compression and spectral unmixing performance of the reconstructed hyperspectral image is obtained. It is noteworthy to mention that the superiority of our method becomes more apparent as the compression ratio grows
Compression and Noise Reduction of Hyperspectral Images using Tucker Decomposition and Discerete Wavelet Transform
No full textInternational audienceThe compression of hyperspectral images (HSIs) becomes recently very attractive issue for remote sensing applications because of their volumetric data. In this paper, an efficient method for joint hyperspectral image compression and noise reduction is presented. The proposed algorithm, based on Discrete Wavelet Transform and Tucker Decomposition (DWT- TD), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply TD on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub images and TD to efficiently compact the energy of sub images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance of the proposed method. Moreover, we show that the compression effects may be viewed to as an efficient noise reduction technique
Hyperspectral image classification using non-subsampled shearlet transform
No full textAbstract: In this paper a new supervised classification method for hyperspectral image is introduced. In the proposed method first, 2D non-subsampled shearlet transform is applied to each spectral band of hyperspectral images. After that, minimum noise fraction transform reduces the dimension of shearlet coefficient sub-bands. Finally, the support vector machine is used for classifying the hyperspectral images based on the extracted features. In order to validate the efficiency of the proposed algorithm, two real hyperspectral image datasets are selected. The obtained classification results are compared with some of the state-of-the-art classification algorithms and the proposed method has reached the highest classification accuracy
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