A Trous Wavelet Transform Research Articles

A Novel Multispectral, Panchromatic and SAR Data Fusion for Land Classification

Multisensor data fusion is addressed in this article for land classification purposes in a semiarid environment. A novel algorithm based on multispectral, panchromatic and synthetic aperture radar (SAR) data is here presented. The proposed multisensory data fusion approach relies on the generalized intensity-hue-saturation (G-IHS) transform and the A trous wavelet transform (ATWT). The fusion product is obtained by modulating the high features details of the panchromatic ATWT with the SAR texture and by replacing the high-pass details of the G-IHS Intensity component with this panchromatic-SAR modulation. After the fusion product is derived, a classification is performed by using a standard maximum likelihood classifier. The proposed algorithm is tested over a meaningful case study acquired over the Maspalomas Special Natural Reserve (Spain) and processing data from WorldView-2 (for both multispectral and panchromatic channels) and TerraSAR-X (for the SAR channel) missions. Results show a fine preservation of the spectral information contained in each multispectral band. Sharpened details are observed over built-up areas and a smoothing texture is perceived over homogeneous areas (lakes, sea, bare soil, and roads) due to the SAR-panchromatic modulation. This leads to a better overall classification accuracy of the fused image compared to outcomes obtained with a single sensor, resulting 7% and 2% more accurate than multispectral and pan-sharpening classification, respectively.

Comparison of Pixel and Object Oriented Based Classification of Hyperspectral Pansharpened Images

In this paper pixel-based and object-oriented classifications were investigated for land-cover mapping in an urban area. Since the image fusion methods are playing a useful role in supplying classification different fusion approaches such as Gram-Schmidt Transform (GS), Principal Component Transform (PC), Haar wavelet, and A Trous Wavelet Transform (ATWT) algorithms have been used and the fused image with the best quality has been assessed on its respected classification. A Hyperion image and IRS-PAN image covering a region near Tehran, Iran have been used to demonstrate the enhancement and accuracy assessment of fused image over the initial images. The evaluation results of fused images showed that the Haar wavelet approach has good quality in preserving spectral information as well as spatial information. Classification results were compared to evaluate the effectiveness of the two classification approaches. Result of the pan-sharpened image classifications displayed that the object-oriented procedure presented more accurate outcomes (90.47 %) than those obtained by pixel-based classification method (77.33 %).

À Trous Wavelet Based Four-Dimensional Evapotranspiration Assimilation

Generalized Gaussian distribution (GGD) can more accurately model the uncertainties involved in evapotranspiration assimilation than Normal distribution for ensemble based assimilation methods. In this paper, GGD derived perturbing samples are introduced to track the flow characteristics of the uncertainties in four-dimensional variational assimilation (4Dva) after the parameters of GGD are estimated from the coefficients of a trous wavelet transform (AWT) using global convergence method. For dissecting the contribution of the multiscale nature of the uncertainties on 4Dva, cost function is in advance converted with AWT from time domain to frequency domain by using wavelet profile as the constraint. Then, the unmatch of background field from model solution is approximated with a linear combination of the perturbing samples, with which the implicit optimization problem is transformed into an explicit one. Finally, assimilated variables are updated by executing multiscale Kalman filter in frequency domain. The outlined method avoids the tangent linear models required in 4Dva so that it can be easily implemented. Evapotranspiration assimilations in Noah prove that the outlined method performs much better than ensemble Kalman filter and 4Dva derived 4DLETKF when there are errors in background initial field or the forcing.

MTF-Adjusted Pansharpening Approach Based on Coupled Multiresolution Decompositions

Among others, the wavelet-based pansharpening approach tries to enhance the resolution of the multispectral (MS) image by injection of spatial details extracted from the high-resolution panchromatic (PAN) image. The problem is presented as follows, the inputs are a coarse-resolution MS image and a high-resolution detail image provided from the PAN image; therefore, one would think that the wavelet reconstruction allows combining approximations and details to construct the high-resolution MS image. However, the wavelet transform (WT) assumes that details and approximations are calculated using the same wavelet decomposition. Now, in the pansharpening case, the MS low-resolution image is assumed to be aliased and blurred due to the imaging system modulation transfer function (MTF) that is approximated as a specific low-pass filter. Meanwhile, there are no constraints about details that can be extracted from PAN using discrete WT (DWT). Approximation and details are not any more orthogonal as needed in the reconstruct of the MS high-resolution image based on DWT. For that, we propose in this paper a new fusion schema [coupled multiresolution decomposition model (CMD)] allowing the reconstruction of a high-resolution MS given its approximation and details obtained by MTF-tailored downsampling and wavelet decomposition, respectively. For validation, CMD is applied to Pleiades, GeoEye-1, and SPOT 6 images. Compared to other approaches [i.e., Gram–Schmidt (GS) adaptive, GS mode 2 (GS2), “A trous’ WT (AWT), generalized Laplacian pyramid (GLP), DWT, and PCI Geomatics software algorithm], our method performs generally better.

Quality assessment of ZiYuan-3 multispectral and panchromatic images fusion: applied in Jiangsu coastal wetland area, China

The new launched ZiYuan-3 (ZY-3) satellite with multispectral (MS) bands and a panchromatic (PAN) band has presented a new opportunity to assess image fusion methods for coastal wetland mapping. This study focuses on image fusion quality assessment through both quantitative spectral and spatial quality analysis and object-oriented classification comparison. Various methods for pan-sharpening ZY-3 MS and PAN bands are tested, including generalized intensity-hue-saturation transform (GIHS), a trous wavelet transform (AWT), nonsubsampled contourlet transform (NSCT), and a combination of NSCT with GIHS (NSCT_GIHS). Spectral fidelity and spatial preservation of fused bands are compared with the original MS bands as reference, and spatial information injections of fused bands are compared with the resampled PAN band as reference. The fusion results demonstrate that, on average, the NSCT_GIHS method has the best performance on spectral fidelity and spatial preservation as well as spatial information injection. The near-infrared (NIR) band has the best spatial information injection in terms of entropy and cross-entropy indices, whereas the NIR band has the best spatial preservation in terms of entropy and structure similarity indices. The classification results show that NSCT_GIHS method also obtains the highest overall accuracy (96%) and Kappa coefficient (0.9425); this is in agreement with the quantitative analysis.

Image retrieval using improved texton co-occurrence matrix

This paper proposed a new technique for content-based image retrieval (CBIR) using a combination of a` trous wavelet transform (AWT) and Julesz|s texton elements. AWT is used to decomposse the image into different scales and different texton elements are used to detect the spatial co-relation among the transform coefficients in horizontal, vertical, diagonal and minor diagonal directions which results into texton image. Further co-occurance operations are performed on texton image in horizontal and zigzag direction. Texton co-occurance matrix gives second order statistics related to texton image. The proposed method is tested on natural database (Corel 1000 and Corel 2500) and texture database (Brodatz and USC texture database) and the retrieval results have demonstrated significant improvement in average precision, average recall rate, as well as feature extraction and retrieval time compared to optimal quantised wavelet correlogram (OQWC) and Gabor wavelet correlogram (GWC).

SAR and Multispectral Image Fusion Using Generalized IHS Transform Based on à Trous Wavelet and EMD Decompositions

In the intensity hue saturation (IHS) based image fusion, the enhancement of the spatial information often leads to the distortion of the information in the spectral domain. In this paper, a spectral preserve fusion method is developed by introducing a trous wavelet transform (AWT) and empirical mode decomposition (EMD) into one generalized IHS transform, which extends panchromatic sharpening of low-resolution multispectral images (LRMIs) through detail injection to the fusion of LRMIs and synthetic aperture radar imagery (SARI). The original LRMIs are first transformed into one low-resolution intensity component (LRIC) and the details from the SARI are abstracted using AWT. The LRIC and the approximation image of the SARI are fused through an intrinsic mode functions based model. Subsequently, high RIC produced through an inverse AWT is then substituted for the old one. High RMIs are obtained through an inverse IHS transform. Experiments reveal that this approach performs better than the AWT and IHS-AWT-based methods in preserving the spectral information of the LRMIs. The performance of the image fusion is examined both visually and quantitatively.

A new feature for image retrieval using a`trous wavelet transform and textons

In this paper, a combination of a`trous wavelet transform (AWT) and Julesz's texton theory is used for feature extraction and retrieval of the images from natural image database. AWT is used to decompose the image and different texton elements are used to detect the spatial co-relation among the transform pixels in horizontal, vertical, diagonal and minor diagonal directions. Further, this information is combined with texture oriented image for generation of image feature vector. The proposed method is tested on Corel 1000 and 2500 image database and the retrieval results have demonstrated significant improvement in average precision, average weighted precision, average recall rate, average rank, standard deviation of rank, standard deviation of precision as well as feature extraction and retrieval time compared to optimal quantised wavelet correlogram (OQWC) and Gabor wavelet correlogram (GWC).