Ciro D'Elia

Università degli studi di Cassino e del Lazio Meridionale, Cassino, Latium, Italy

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Publications (30)16.12 Total impact

  • C. D'Elia, S. Ruscino
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    ABSTRACT: In this paper we propose a method to perform automated radiometric correction of remotely sensed multispectral hyperspectral images. The effects of atmosphere, as well as the calibration errors which the satellite sensors may present, may be compensated by performing the radiometric correction operation in order to achieve good performances in different applications, such as classification and change detection. As far as the change detection is concerned, relative radiometric correction is particularly interesting since it deals with images which have to be compared and since in this context an absolute correction may be characterized by a high complexity. One method for performing radiometric correction of multispectral images can be based on a least-square approach: considering one image as the reference one and the other as a linearly scaled version of the reference one, the linear coefficients can be calculated by using a set of control points conveniently chosen. Unfortunately, the choice of control points is a tricky operation, strictly connected to the specific application. In this paper we propose an automated method for performing relative radiometric correction of multispectral remotely sensed images, in which the choice of the control points is based on a comparison of the spectral content of those images to the spectral response of known materials. Specifically, we perform a vector quantization of the images separately, considering N quantization levels represented by N known materials' signatures properly selected. Then the quantized images are compared in order to identify the areas classified as belonging to the same class, so identified by the same quantization index which will make the subset of control points that should be used for performing relative radiometric correction. Experimental results showed that choosing points characterized by an homogeneous spectral content for radiometric correction improves the performances of specific image processing algorithms, such as change detection and classification algorithms.
    Proc SPIE 11/2012;
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    ABSTRACT: In the last years, the extraction of the information content from digital images has assumed a crucial role in many application fields, such as risk assessment, analysis of damages, deforestation, environmental monitoring, earth observation, as a fundamental instrument to carry out specific and pointed studies. In this context the change detection of remotely sensed images takes place. Detecting changes means performing a spatial comparison of two or more images acquired over the same geographical area at different times. This operation can be performed on a per-pixel basis as well as on a per-object basis, depending on the aim of the specific application. In particular, in this paper two versions of the same change detection algorithm are presented, the one working on a per-pixel basis while the other working on an per-object basis, applied specifically for the monitoring of a water supply infrastructure. This algorithm provides the changes occurred in optical images' spectral content, as well as in their radiance content, by calculating two change features: the spectral angle made by two corresponding spectral vectors in the compared images, and the so-called Brightness Change Factor. The object-based version of the presented change detection algorithm has been developed according to an IIM - Image Information Mining context, in order to introduce an automated procedure to detect changes; furthermore, it has been developed with an a image analysis framework, called IPAINT - Image Processing Analysis Interpretation and Trasconding, which can be used for various applications thanks to its versatility, since it offers many different techniques for image processing.
    Advances in Radar and Remote Sensing (TyWRRS), 2012 Tyrrhenian Workshop on; 01/2012
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    ABSTRACT: This paper describes the most recent achievements in speckle reduction of COSMO-SkyMed (CSK@) synthetic aperture radar (SAR) data. An advanced multiresolution despeckling filter, based on undecimated wavelet transform (UDWT) and maximum a-posteriori (MAP) estimation has been specialized and optimized to CSKê data, both single- and multi-look. The tradeoff between performances and computational complexity has been investigated: Laplacian-Gaussian and generalized Gaussian (GG) priors for MAP estimation in UDWT domain differ by one order of magnitude in computation cost. Pre-processing of point targets and segmentation of wavelet planes has been exploited to effectively handle the heterogeneity of the data. Besides traditional supervised methods to evaluate the quality of despeckling, a novel procedure, fully automated, based on bivariate analysis of noisy and denoised image has been devised.
    Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
  • Image Analysis and Processing - ICIAP 2011 - 16th International Conference, Ravenna, Italy, September 14-16, 2011, Proceedings, Part II; 01/2011
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    ABSTRACT: The amount of multimedia information generated in today society is growing exponentially. This makes an important purpose of research to evaluate the quality of service in order to achieve customer satisfaction, i.e. in order to improve the quality of experience.
    01/2011;
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    ABSTRACT: The aim of this paper is to describe a novel system for computer-aided detection of clusters of microcalcifications on digital mammograms. Mammograms are first segmented by means of a tree-structured Markov random field algorithm that extracts the elementary homogeneous regions of interest. An analysis of such regions is then performed by means of a two-stage, coarse-to-fine classification based on both heuristic rules and classifier combination. In this phase, we avoid taking a decision on the single microcalcifications and forward it to the successive phase of clustering realized through a sequential approach. The system has been tested on a publicly available database of mammograms and compared with previous approaches. The obtained results show that the system is very effective, especially in terms of sensitivity. The proposed approach exhibits some remarkable advantages both in segmentation and classification phases. The segmentation phase employs an image model that reduces the computational burden, preserving the small details in the image through an adaptive local estimation of all model parameters. The classification stage combines the results of the classifiers focused on the single microcalcification and the cluster as a whole. Such an approach makes a detection system particularly effective and robust with respect to the large variations exhibited by the clusters of microcalcifications.
    Artificial intelligence in medicine 09/2010; 50(1):23-32. · 1.65 Impact Factor
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    ABSTRACT: In this work, maximum a posteriori (MAP) despeckling, implemented in the multiresolution domain defined by the undecimated discrete wavelet transform (UDWT), will carried out on very high resolution (VHR) SAR images and compared with earlier multiresolution approaches developed by the authors. The MAP solution in UDWT domain has been specialized to SAR imagery. Every UDWT subband is segmented into statistically homogeneous segments and one generalized Gaussian (GG) PDF (variance and shape factor) is estimated for each segment. This solution allows to effectively handle scene heterogeneity as imaged by the VHR SAR system. Segmentation exploits a Tree Structured Markov Random Field (TSMRF), which is a low complexity MRF segmentation that allows the estimation of the number of segments and the segmentation itself to be carried out at same time. Experiments performed on a single-look VHR X-band SAR images demonstrate that the segmented approach is effective whenever the classical circular Gaussian model of complex reflectivity may no longer hold.
    IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2010, July 25-30, 2010, Honolulu, Hawaii, USA, Proceedings; 01/2010
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    IJPRAI. 01/2009; 23:887-905.
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    ABSTRACT: Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A particularly significant clue of such disease is the presence of clusters of microcalcifications. The automatic detection and classification of such clusters is a very difficult task because of the small size of the microcalcifications and of the poor quality of the digital mammograms. In literature, all the proposed methods for the automatic detection focus on the single microcalcification. In this paper, an approach that moves the final decision on the regions identified by the segmentation in the phase of clustering is proposed. To this aim, the output of a classifier on the single microcalcifications is used as input data in a clustering algorithms which produce the detected clusters. As final output the system highlights the suspicious clusters, leaving to the specialist the diagnosis responsibility. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.
    Computer-Based Medical Systems, 2008. CBMS '08. 21st IEEE International Symposium on; 07/2008
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    ABSTRACT: In the framework of multiple classifier systems, we suggest to reformulate the classifier combination problem as a pattern recognition one. Following this approach, each input pattern is associated to a feature vector composed by the output of the classifiers to be combined. A Bayesian Network is used to automatically infer the probability distribution for each class and eventually to perform the final classification. We propose to use Bayesian Networks because they not only provide a basis for efficient probabilistic inference, but also a natural and compact way to encode exponentially sized joint probability distributions. Two systems adopting an ensemble of Back-Propagation neural network and an ensemble of Learning Vector Quantization neural network, respectively, have been tested on the Image database from the UCI repository. The performance of the proposed systems have been compared with those exhibited by multi-expert systems adopting the same ensembles, but the Majority Vote, the Weighted Majority vote and the Borda Count for combining them.
    14th International Conference on Image Analysis and Processing (ICIAP 2007), 10-14 September 2007, Modena, Italy; 01/2007
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    IJPRAI. 01/2007; 21:43-59.
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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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    ABSTRACT: We introduce some improvements to the dynamic learning vector quantization algorithm proposed by us for tackling the two major problems of those networks, namely neuron over-splitting and their distribution in the feature space. We suggest to explicitly estimate the potential improvement on the recognition rate achievable by splitting neurons in those regions of the feature space in which two or more classes overlap. We also suggest to compute the neuron splitting frequency, and to combine these information for selecting the most promising neuron to split. Experimental results on both synthetic and real data extracted from UCI Machine Learning Repository show substantial improvements of the proposed algorithm with respect to the state of the art
    18th International Conference on Pattern Recognition (ICPR 2006), 20-24 August 2006, Hong Kong, China; 01/2006
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    Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International; 08/2005
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    ABSTRACT: In this paper the application of a transform coding technique, based on overcomplete independent component analysis (ICA), for the compression of single look intensity synthetic aperture radar (SAR) images is explored. The method has the advantage of representing the image through almost statistically independent coefficients, with an assigned distribution, so that a scalar entropy constrained quantizer, optimized for the coefficients statistics, can be used. Numerical results on ERS-1 data are presented.
    Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International; 08/2005
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    ABSTRACT: SAR images are affected by speckle that affects radiometric resolution and class discrimination capabilities. Recently, different speckle reduction techniques based on maximum a posteriori (MAP) estimation have been proven to have very good performances. These techniques are based on the introduction of an a priori statistical model of the speckle free image to be estimated. We propose a MAP method using more than one sub-band filtered intensity images and a Markov random field (MRF) a priori model. The method has been experimented on simulated and real images
    Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International; 10/2004
  • G. Cuozzo, C. D'Elia, V. Puzzolo
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    ABSTRACT: The forest cover classification is extremely important for land use planning and management. In this framework, the application of pixel based classifications of middle resolution images is well assessed while the usefulness of segmentation processes and object classification is still improving. In this paper, a method based on tree-structured Markov random field (TS-MRF) is applied to Landsat TM images in order to assess the capability of the TS-MRF segmentation algorithm for discriminating forest-non forest covers in a test area located in the Eastern Italian Alps of Trentino. In particular, the regions of interest are selected from the image using a two step process based on a segmentation algorithm and an analysis process. The segmentation is achieved applying a MRF a-prior model, which takes into account the spatial dependencies in the image, and the TS-MRF optimisation algorithm which segments recursively the image in smaller regions using a binary tree structure. The analysis process links to each object identified by the segmentation a set of features related to the geometry (like shape, smoothness, etc.), to the spectral signature and to the neighbour regions (contextual features). These features were used in this study for classifying each object as forest or non-forest thought a simple supervised classification algorithm based on a thresholds built on the feature values obtained from a set of training objects. This method already allowed the detection of the forest area within the study area with an accuracy of 90%, while better performances could be achieved using more sophisticated classification algorithm, like Neural Networks and Support Vector Machine.
    Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International; 10/2004
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    ABSTRACT: At present, mammography is the only not invasive diagnostic technique allowing the diagnosis of a breast cancer at a very early stage. A visual clue of such disease particularly significant is the presence of clusters of microcalcifications. Reliable methods for an automatic detection of such clusters are very difficult to accomplish because of the small size of the microcalcifications and of the poor quality of the digital mammograms. A method designed for this task is described. The mammograms are firstly segmented by means of the tree structured Markov random field algorithm which extracts the elementary homogeneous regions of interest on the image. Such regions are then submitted to a further analysis (based both on heuristic rules and support vector classification) in order to reduce the false positives. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.
    Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on; 01/2004
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    C. D'Elia, G. Poggi, G. Scarpa
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    Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International; 08/2003
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    ABSTRACT: We present a new image segmentation algorithm based on a tree-structured binary MRF model. The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity. To improve segmentation accuracy, a split-and-merge procedure is also developed and a spatially adaptive MRF model is used. Numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on "flat" MRF models, and its performance, in terms of segmentation accuracy and map smoothness, is comparable or even superior.
    IEEE Transactions on Image Processing 02/2003; 12(10):1259-73. · 3.20 Impact Factor

Publication Stats

139 Citations
16.12 Total Impact Points

Institutions

  • 2003–2008
    • Università degli studi di Cassino e del Lazio Meridionale
      • Department of Automation, Electromagnetism, Information Engineering and Industrial Mathematics - DAEIMI
      Cassino, Latium, Italy
  • 2000–2003
    • University of Naples Federico II
      • Department of Electronical Engineering and Telecommunications
      Napoli, Campania, Italy
  • 2001
    • Naples Eastern University
      • Dipartimento di Ingegneria Elettronica
      Napoli, Campania, Italy