Roberto Carlà

Assessment of pyramid-based multisensor image data fusion

This paper reports about a quantitative evaluation of pyramid- based schemes performing a feature-based fusion of data from multispectral and panchromatic imaging sensors having different ground resolutions. A critical point is performances evaluation of image data fusion. A set of quantitative parameters has been recently proposed. Both visual quality, regarded as contrast, presence of fine details, and absence of impairments and artifacts (e.g., blur, ringing), and spectral fidelity (i.e., preservation of spectral signatures) are concerned and embodied in the measurements. The aim of the present work is to provide a comprehensive performance comparison on SPOT data among three feature-based schemes for image fusion, as well as on a specific case study on which multisensor observations were available. Out of the three methods compared, respectively based on high-pass filtering (HPF), wavelet transform (WT), and generalized Laplacian pyramid (GLP), the latter two are far more efficient than the former, thus establishing the advantages for data fusion of a formally multiresolution analysis.

Two-dimensional rank-conditioned median filter

A version of 2-D median filter specialized to impulse noise removal introducing negligible distortion in noise-free pixels is proposed. Only pixels of prefixed ranks are replaced with the local median. When extreme ranks (min and max) are chosen, spikes are selectively suppressed, more efficiently than by median filter. Error probability for salt/pepper noise is theoretically derived. MAE and visual comparisons with median filter attest increased accuracy. >

Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets

In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred.

Pyramid-based multisensor image data fusion

In this work, a multi-resolution procedure based on a generalized Laplacian pyramid (GLP), with p:q (i.e. rational) scale factor, is proposed to merge image data of any resolution and represent them at any scale. The GLP- based data fusion is shown to be slightly superior to those of a similar scheme based on the discrete wavelet transform (WT), according to a set of parameters established in the literature. Not only fused images look sharper than their original versions, but also textured regions are enhanced without losing their spectral signatures. The pyramid- generating filters can be easily designed for data of any resolutions, differently from the WT, whose filter-bank design is non-trivial when the ratio between the scales of the images to be merged is not a power of two. Eventually, remotely sensed images from LandSat TM and from Panchromatic SPOT are fused together. The resulting bands capture multi- spectral features with enhanced contrast and texture, and an increased spatial resolution, thereby expediting automatic analyses for contextual interpretation of the environment.

Principal component analysis for change detection on polarimetric multitemporal SAR data

Principal component analysis (PCA) is applied to investigate on changes occurring in multitemporal polarimetric SAR imagery. Correlation instead of covariance matrix is used in the transformation, thus reducing gain variations introduced by the imaging system and giving equal weight to each polarization. The approach is effective when PCA is computed on images recorded simultaneously, as well as when it is applied to the whole set of multitemporal images.<<ETX>>

Full-Scale Assessment of Pansharpening Through Polynomial Fitting of Multiscale Measurements

Pansharpening techniques aim at improving the spatial resolution of a multispectral data set (MS) by using a panchromatic image (Pan) acquired on the same scene with a greater spatial resolution and consequently a lower ground sample distance (GSD). Usually, a quantitative assessment of the fused products cannot be directly performed because of the lack of a reference MS data set with the same GSD of the Pan. A well-known solution is Walds protocol: The original MS and Pan are spatially degraded, the reducing factor being the ratio between their GSDs. Pansharpening is then performed between the reduced MS and Pan data sets, and the fused products are compared with the original MS, which can be used as reference. In this protocol, fusion performances are assumed to be independent of the scale so that the results at the reduced scale are an estimation of those at the original resolution. This hypothesis can be more or less reliable, depending on the sensor and/or the scene content. The objective of this paper is to propose a new methodology to infer the unknown performances of a pansharpening method at full scale. For this purpose, multiple sets of fused images are computed at degraded scales by downsampling Pan and MS data sets by means of the sensor modulation transfer function. Multiscale quality/distortion measurements are fitted by linear and quadratic polynomials in order to extrapolate their full-scale values. Once the proposed protocol has been assessed in the presence of reference originals, the obtained results are extended to the case where the reference image is not available.

Adaptive texture-preserving filtering of multitemporal ERS-1 SAR images

A novel approach is proposed to adaptive filtering of multi-temporal images. A transformation aimed at decorrelating data is defined on the set of images taken at different times. Adaptive filtering is performed on the new set of data which are then transformed back. Results on ERS-1 multi-temporal SAR images show effectiveness in reducing speckle, while the intrinsic texture, edges and point targets are better preserved than by filtering each observation separately.

Pyramid approach to fusion of Landsat TM and SPOT-PAN data to yield multispectral high-resolution images for environmental archaeology

Several attempts have been made to merge Landsat TM multispectral data with high spatial resolution panchromatic SPOT images. In this work a multiresolution approach based on a generalized Laplacian pyramid with p:q (i.e., rational) scale factor is proposed to merge image data of any resolution and represent them at any scale. The resulting bands capture multispectral characteristics with an enhanced spatial resolution, thereby expediting visual analysis and contextual interpretation of the environment according to archaeological issues. Objective and subjective evaluations show the effectiveness of the proposed method.

Influence of spatial resolution on pan-sharpening results

Pan-sharpening techniques improve the spatial resolution of a Multispectral image (MS) by using a Panchromatic image (PAN) of the same scene contemporaneously acquired at higher resolution. Usually, a quantitative assessment of the resulting fused MS image cannot be directly performed because of the lack of a reference MS. Walds protocol offers a possible solution: original Pan and MS are spatially degraded, the reducing factor being the ratio between their resolutions. Pan-sharpening is then performed between the reduced MS and PAN images. The quality of the fused products is then evaluated by comparing them with the original MS used as reference, by assuming the hypothesis that the performances of the pan-sharpening methods are independent from scale. The objective of this work is to propose a methodology to verify this hypothesis. For this aim, pan-sharpening performances when varying spatial resolution are investigated and a viable strategy to devise pansharpening performances at full scale is suggested.

Full scale assessment of pansharpening methods and data products

Quality assessment of pansharpened images is traditionally carried out either at degraded spatial scale by checking the synthesis property ofWald’s protocol or at the full spatial scale by separately checking the spectral and spatial consistencies. The spatial distortion of the QNR protocol and the spectral distortion of Khan’s protocol may be combined into a unique quality index, referred to as hybrid QNR (HQNR), that is calculated at full scale. Alternatively, multiscale measurements of indices requiring a reference, like SAM, ERGAS and Q4, may be extrapolated to yield a quality measurement at the full scale of the fusion product, where a reference does not exist. Experiments on simulated Pl´eiades data, of which reference originals at full scale are available, highlight that quadratic polynomials having three-point support, i.e. fitting three measurements at as many progressively doubled scales, are adequate. Q4 is more suitable for extrapolation than ERGAS and SAM. The Q4 value predicted from multiscale measurements and the Q4 value measured at full scale thanks to the reference original, differ by very few percents for six different state-of-the-art methods that have been compared. HQNR is substantially comparable to the extrapolated Q4.