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ABSTRACT: Automated change analysis of multi-temporal SAR images is a challenging task due to the inherent noisiness of SAR imagery and the variability of the backscattering coefficient to the acquisition angle. Several methods have been proposed in the literature to improve the change detection performances with respect to the classical method based on the Log-Ratio operator. In this paper a pixel change feature is proposed and tested on true Cosmo-SkyMed detected images for damage assessment applications. The method does not require any despeckling pre-processing and is robust both to the acquisition noise and to possible variation of the acquisition angle in the two observations.
Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), 2011 6th International Workshop on the; 08/2011
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ABSTRACT: In this paper, the characteristics of multispectral (MS) and panchromatic (P) image fusion methods are investigated. Depending on the way spatial details are extracted from P, pansharpening methods can be broadly labeled into two main classes, corresponding to methods based on either component substitution (CS) or multiresolution analysis (MRA). Theoretical investigations and experimental results evidence that CS-based fusion is far less sensitive than MRA-based fusion to: 1) registration errors, i.e., spatial misalignments between MS and P images, possibly originated by cartographic projection and resampling of individual data sets; 2) aliasing occurring in MS bands and stemming from modulation transfer functions (MTF) of MS channels that are excessively broad for the sampling step. In order to assess the sensitiveness of methods, aliasing is simulated at degraded spatial scale by means of several MTF-shaped digital filters. Analogously, simulated misalignments, carried out at both full and degraded scale, evidence the quality-shift tradeoff of the two classes. MRA yields a slightly superior quality in the absence of aliasing/misalignments, but is more penalized than CS, whenever either aliasing or shifts between MS and P occur. Conversely, CS generally produces a slightly lower quality, but is intrinsically more aliasing/shift tolerant.
IEEE Journal of Selected Topics in Signal Processing 07/2011; · 2.88 Impact Factor
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ABSTRACT: This paper deals with an original method suitable for estimating the noise introduced by optical imaging systems, such as CCD cameras, multispectral scanners and imaging spectrometers. The power of the signal-dependent photonic noise is decoupled from that of the signal-independent noise generated by the electronic circuitry. The method relies on the multivariate regression of local sample mean and variance. Statistically homogeneous pixels produce scatter-points that are clustered along a straight line, whose slope and intercept measure the signal-dependent and signal-independent components of the noise power, respectively. Experimental results carried out on a simulated noisy image and on true data from a modern generation airborne imaging spectrometer highlight the accuracy of the proposed method and its robustness to textures that may lead to a gross over-estimation of the noise, for high SNR.
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS '09. First Workshop on; 09/2009
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ABSTRACT: In this letter a novel method suitable for the lossless compression of hyperspectral imagery is presented. The proposed method generalizes two previous algorithms, in which the concept of nearest neighbor (NN) prediction implemented through either one or two lookup tables (LUTs) was introduced. Now M LUTs are defined on each of the N previous bands, from which prediction is calculated. The decision among one of the N middot M possible prediction values is based on the closeness of the values contained in the LUTs to an advanced prediction carried out from the values in the same N previous bands. Such a prediction is provided by either of two spectral predictors recently developed by the authors. Experimental results carried out on the AVIRIS'97 data set show improvements up to 18% over the baseline LUT-NN algorithm. However, preliminary results carried out on raw data show that all LUT-based methods are not suitable for on-board compression, since they take advantage uniquely of the data artifacts that may be originated by the on-ground calibration procedure.
IEEE Signal Processing Letters 07/2009; · 1.39 Impact Factor
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ABSTRACT: Multiresolution analysis (MRA) and component substitution (CS) are the two basic frameworks to which image fusion algorithms can be reported when merging multispectral (MS) and panchromatic (Pan) images (pansharpening), acquired with different spatial and spectral resolutions. State-of-the-art algorithms add the spatial details extracted from the Pan into the MS data set by considering different injection strategies. The capability of efficiently modeling the relationships between MS and Pan is crucial for the quality of fusion results and particularly for a correct recovery of local features with a consequent reduction of spectral distortions. Although context-adaptive (CA) injection models have been proposed in the MRA framework, their adoption in CS schemes has been scarcely investigated so far. In this letter, CA strategies are compared with global models by considering a general protocol in which both MRA- and CS-based schemes can be described. Qualitative and quantitative results are reported for three high-resolution data sets from two different sensors, namely, IKONOS and simulated Pleiades. The score gains of well-known and novel quality figures show that CA models are more efficient than global ones.
IEEE Geoscience and Remote Sensing Letters 05/2009; · 1.56 Impact Factor
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ABSTRACT: This paper presents a novel algorithm suitable for the lossless compression of hyperspectral imagery. The algorithm generalizes two previous algorithms, in which the concept nearest neighbor (NN) prediction implemented through lookup tables (LUTs) was introduced. Here, the set of LUTs, two or more, say M, on each band are allowed to span more than one previous band, say N bands, and the decision among one of the NM possible prediction values is based on the closeness of the value contained in the LUT to an advanced prediction, spanning N previous bands as well, provided by a top-performing scheme recently developed by the authors and featuring a classified spectral prediction. Experimental results carried out on the AVIRIS '97 dataset show improvements up to 15% over the baseline LUT-NN algorithm. However, preliminary results carried out on raw data show that all LUT-based methods are not suitable for on-board compression, since they take advantage uniquely of the sparseness of data histograms, which is originated by the on-ground calibration procedure.
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International; 08/2008
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ABSTRACT: In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach. A general scheme that is capable of modeling any CS image fusion method is presented and discussed. According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. The weights are obtained as regression coefficients between the MS bands and the spatially degraded panchromatic (Pan) image, with the aim of capturing the spectral responses of the sensors. Once it has been integrated into the Gram-Schmidt spectral-sharpening method, which is implemented in environment for visualizing images (ENVI) program, and into the generalized intensity-hue-saturation fusion method, the proposed preprocessing module allows the production of fused images of the same spatial sharpness but of increased spectral quality with respect to the standard implementations. In addition, quantitative scores carried out on spatially degraded data clearly confirm the superiority of the enhanced methods over their baselines.
IEEE Transactions on Geoscience and Remote Sensing 11/2007; · 2.89 Impact Factor
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ABSTRACT: This letter presents an original approach that exploits classified spectral prediction for lossless/near-lossless hyperspectral-image compression. Minimum-mean-square-error spectral predictors are calculated, one for each small spatial block of each band, and are classified (clustered) to yield a user-defined number of prototype predictors that are capable of matching the spectral features of different classes of pixel spectra for each wavelength. Such predictors are used to achieve a prediction, either crisp or fuzzy. Unlike most of the methods reported in the literature, the proposed approach exploits a purely spectral prediction that is suitable in compressing the data in band-interleaved-by-line format, as they are available at the output of the onboard instrument. In that case, the training phase, i.e., clustering and refining of predictors for each wavelength, may be moved offline. Experimental results on Airborne Visible InfraRed Imaging Spectrometer data show improvements over the most advanced methods in the literature, with a computational complexity that is far lower than that of analogous methods by the same and other authors.
IEEE Geoscience and Remote Sensing Letters 11/2007; · 1.56 Impact Factor
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ABSTRACT: This paper presents two novel image fusion methods, suitable for sharpening of hyperspectral (HS) images by means of a panchromatic (Pan) observation: the HS bands expanded to the finer scale of the Pan image are sharpened by adding the spatial details which are calculated by the PAN image. Since a direct, unconditioned injection of Pan details gives unsatisfactory results, a new injection model is proposed, which provides the optimum injection simulating fusion at degraded scale by minimizing the mean square error. Fusion tests are carried out both on spatially degraded data to objectively compare the proposed scheme to some fusion methods and on full resolution image data.
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International; 08/2007
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ABSTRACT: Multi-temporal analysis of Synthetic Aperture Radar (SAR) images has gained an ever increasing attention due to the availability of several satellite platforms with different revisit times and to the intrinsic capability of the SAR system of producing all-weather observations. As a drawback, automated analysis in general and change detection in particular are made difficult by the inherent noisiness of SAR imagery. Even if a pre-processing step aimed at speckle reduction is adopted, most of algorithms borrowed from computer vision cannot be profitably used. In this work, a novel pixel feature suitable for change analysis is derived from information-theoretic concepts. It does not require preliminary de-speckling and capable of providing accurate change maps from a couple of SAR images. The rationale is that the negative of logarithm of the probability of an amplitude level in one image conditional to the level of the same pixel in the other image conveys an information on the amount of change occurred between the two passes. Experimental results carried out on two couples of multi-temporal SAR images demonstrate that the proposed IT feature outperform the Log-Ratio in terms of capability of discriminating changes.
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International; 08/2007
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ABSTRACT: In this work, a multi-resolution procedure based on a generalized Laplacian pyramid (GLP) with a 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 superior to those of a similar scheme based on the discrete wavelet transform (WT) according to a set of parameters established in the literature. 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. Remotely sensed images from Landsat TM and from Panchromatic SPOT are fused together. Textured regions are enhanced without losing their spectral signatures, thereby expediting automatic analyses for contextual interpretation of the environment.
11/2006: pages 87-94;
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Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on; 09/2006
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ABSTRACT: Segmentation algorithms are often used in many image processing applications like compression, restoration, content extraction, and classification. In particular as for content extraction works carried out in the past decade have demonstrated that multi-frequency fully polarimetric SAR observations are particularly interesting, thanks to physical properties of the backscattered signal at various frequencies and polarizations. To achieve a good classification, the main difficulty is that SAR images are often embedded in heavy speckle. Segmentation of multi/hyperspectral (optical) imagery is obtained by means of algorithms based on image models, which exploit the spatial dependencies of land-covers. Unfortunately, speckle noise hides such spatial dependencies in observed SAR data. With the aim of investigating on a content extraction algorithm capable of discriminating cover classes present in the observed SAR image, heterogeneity features are used here to emphasize spatial dependencies in the data. Thus, observed pixel values are mapped into features, that take "similar" values on "similar" textures. This allows for using the same procedure of the optical case. Obviously, homogeneity/heterogeneity feature and segmentation quality are fundamental for classification accuracy. Here, the problem is tackled through the joint use of information theoretic SAR features and of a segmentation algorithm based on Markov Random Fields (MRFs).
Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on; 09/2006
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Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on; 09/2006
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ABSTRACT: In this work we investigate the use of Shannon's information theory for the goal of devising quality scores of image fusion results that do not require reference originals. In particular, the mutual information between resampled original and fused MS bands is used to measure the spectral quality, while the mutual information between the Pan image and the fused bands yields a measure of spatial quality. The rationale is that the normalized mutual information calculated either between any couple of bands, or between each MS band and the Pan image, should be unchanged after fusion, i.e., when passing from the coarse scale of the MS data to the fine scale of the Pan image. Experimental results carried out on QuickBird and Ikonos data demonstrate that the results provided by the proposed information-theoretic method are in trend with analysis performed on spatially degraded data by means of such parameters as Walds's ERGAS, Wang and Bovik's QI, and the novel Q4 score index based on quaternion theory and recently proposed by the authors. However, the novel method requires no reference and is therefore directly applicable in all practical cases
Information Fusion, 2006 9th International Conference on; 08/2006
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Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International; 08/2005
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Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International; 08/2005
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ABSTRACT: This paper presents a novel scheme for lossless/near-lossless hyperspectral image compression, that exploits a classified spectral prediction. MMSE spectral predictors are calculated for small spatial blocks of each band and are classified (clustered) to yield a user-defined number of prototype predictors for each wavelength, capable of matching the spatial features of different classes of pixel spectra. Unlike most of the literature, the proposed method employs a purely spectral prediction, that is suitable for compressing the data in band-interleaved-by-line (BIL) format, as they are available at the output of the on-board spectrometer. In that case, the training phase, i.e., clustering of predictors for each wavelength, may be moved off-line. Thus, prediction will be slightly less fitting, but the overhead of predictors calculated on-line is saved. Although prediction is purely spectral, hence ID, spatial correlation is removed by the training phase of predictors, aimed at finding statistically homogeneous spatial classes matching the set of prototype spectral predictors. Experimental results on AVIRIS data show improvements over the most advanced methods in the literature, with a computational complexity far lower than that of analogous methods by other authors.
Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International; 08/2005
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ABSTRACT: A heterogeneity feature, calculable from synthetic aperture radar (SAR) images on a per-pixel basis, but relying on global image statistics, is defined and discussed. Starting from the multiplicative speckle and texture models relating the amount of texture and speckle to the local mean and variance at every pixel, such a feature is rigorously derived from Shannon's information theory as the conditional information of local standard deviation to local mean. Thanks to robust statistical estimation, it is very little sensitive to the noise affecting SAR data, and thus capable of capturing subtle variations of texture whenever they are embedded in a heavy speckle. Experimental results carried out on two SAR images with different degrees of noisiness demonstrate that the proposed feature is likely to be useful for a variety of automated segmentation and classification tasks.
IEEE Transactions on Geoscience and Remote Sensing 04/2005; · 2.89 Impact Factor
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ABSTRACT: This work presents a novel multisensor image fusion algorithm, which extends panchrmomatic sharpening of multispectral (MS) data through intensity modulation to the integration of MS and synthetic aperture radar (SAR) imagery. The method relies on SAR texture, extracted by ratioing the despeckled SAR image to its low-pass approximation. SAR texture is used to modulate the generalized intensity (GI) of the MS image, which is given by a linear transform extending intensity-hue-saturation transform to an arbitrary number of bands. Before modulation, the GI is enhanced by injection of high-pass details extracted from the available panchrmomatic image by means of the "a`-trous" wavelet decomposition. The texture-modulated panchrmomatic-sharpened GI replaces the GI calculated from the resampled original MS data. Then, the inverse transform is applied to obtain the fusion product. Experimental results are presented on Landsat-7 Enhanced Thematic Mapper Plus and European Remote Sensing 2 satellite images of an urban area. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, which can be usefully applied for both visual analysis and classification purposes.
IEEE Transactions on Geoscience and Remote Sensing 01/2005; · 2.89 Impact Factor
