Dr. Palma N. Blonda Profile

Dr. Palma N. Blonda

Researcher at Consiglio Nazionale delle Ricerche

SPIE Involvement:

Author

Proceedings Article | 10 November 2004 Paper

Change detection of man-induced landslide causal factors

Cristina Tarantino, Palma Blonda, Guido Pasquariello

Proceedings Volume 5573, (2004) https://doi.org/10.1117/12.565811

KEYWORDS: Landslide (networking), Remote sensing, Agriculture, Landsat, Neural networks, Spatial resolution, Integrated optics, Image classification, Natural disasters, Earth observing sensors

Read Abstract +

Proceedings Article | 15 May 2003 Paper

Detection of multiple sclerosis lesions in MRIs with an SVM classifier

Annarita D'Addabbo, Nicola Ancona, Palma Blonda, Roberto De Blasi

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.480407

KEYWORDS: Magnetic resonance imaging, Image classification, Machine learning, Magnetism, Neural networks, 3D image processing, Tissues, Data acquisition, Pathology, Visualization

Read Abstract +

SPIE Journal Paper | 1 April 2000

Contextual clustering for image segmentation

Andrea Baraldi, Palma Blonda, Flavio Parmiggiani, Giuseppe Satalino

OE, Vol. 39, Issue 04, (April 2000) https://doi.org/10.1117/12.10.1117/1.602467

KEYWORDS: Image segmentation, Picture Archiving and Communication System, Image processing algorithms and systems, Expectation maximization algorithms, Scanning electron microscopy, Image classification, Magnetorheological finishing, Data modeling, Optical engineering, Spectral models

Proceedings Article | 14 December 1999 Paper

Neural techniques for SAR intensity and coherence data classificataion

Palma Blonda, Giuseppe Satalino, Janusz Wasowski, Mario Parise, Alberto Baraldi, Alberto Refice

Proceedings Volume 3871, (1999) https://doi.org/10.1117/12.373257

KEYWORDS: Synthetic aperture radar, Data modeling, Interferometry, Image classification, Landslide (networking), Backscatter, Coherence (optics), Quantization, Feature extraction, Classification systems

Read Abstract +

In recent years it has been proved that combined analysis of SAR intensity and interferometric correlation images is a valuable tool in classification tasks where traditional techniques such as crisp thresholding schemes and classical maximum likelihood classifiers have been employed. In this work, developed in the framework of the ESA AO3-320 project titled Application of ERS data to landslide activity monitoring in southern Apennines, Italy, our goal is to investigate: (1) usefulness of SAR interferometric correlation information in mapping areas with diffuse erosional activity, including landslides; and (2) effectiveness of soft computing techniques in the combined analysis of SAR intensity and interferometric correlation images. Two neural classifiers are selected from the literature. The first classifier is a one- stage error-driven Multilayer Perceptron (MLP) and the second classifier is a Two-Stage Hybrid (TSH) learning system, consisting of a sequence of an unsupervised data-driven first stage with a supervised error-driven second stage. The TSH unsupervised first stage is implemented as either: (1) the on- line learning, dynamic-sizing, dynamic-linking Fully Self Organizing Simplified Adaptive Resonance Theory (FOSART) clustering model; (2) the batch-learning, static-sizing, no- linking Fuzzy Learning Vector Quantization (FLVQ) algorithm; or (3) the on-line learning, static-sizing, static-linking Self-Organizing Map (SOM). The input data set consists of three SAR ERS-1/ERS-2 tandem pair images depicting an area featuring slope instability phenomena in the Campanian Apennines of Southern Italy. From each tandem pair, four pixel-based features are extracted: the backscattering mean intensity, the interferometric coherence, the backscattering intensity texture and the backscattering intensity change. Our classification task is focused on the discrimination of land cover types useful for hazard evaluation, i.e., evaluation of areas affected by erosion. Classification results show that class erosion can be discriminated from other land cover classes when SAR mean intensity images are combined with coherence and texture information. In addition, our results demonstrate that soft computing techniques provide useful tools for the combined analysis of SAR intensity and coherence images. In particular, the TSH classifier employing the FOSART clustering algorithm shows: (1) an overall accuracy comparable with that of the other classification schemes under testing; (2) a training cost significantly lower than that of MLP and lower than that of TSH employing either FLVQ or SOM as its first stage; and (3) a capability of discriminating class erosion superior to that of the other classification schemes under testing.

Proceedings Article | 20 November 1998 Paper

Local and global strategies for InSAR phase unwrapping

Alberto Refice, Maria Chiaradia, Luciano Guerriero, Giovanni Nico, Palma Blonda, Guido Pasquariello, Giuseppe Satalino, Sebastiano Stramaglia, Nicola Veneziani

Proceedings Volume 3497, (1998) https://doi.org/10.1117/12.331345

KEYWORDS: Interferometric synthetic aperture radar, Stochastic processes, Neural networks, Interferometry, Reconstruction algorithms, Algorithms, Annealing, Coherence (optics), Control systems, Phase measurement

Read Abstract +

Proceedings Article | 13 October 1998 Paper

Detailed comparison of neuro-fuzzy estimation of subpixel land-cover composition from remotely sensed data

Andrea Baraldi, Elisabetta Binaghi, Palma Blonda, Pietro Brivio, Anna Rampini

Proceedings Volume 3455, (1998) https://doi.org/10.1117/12.326731

KEYWORDS: Fuzzy logic, Vegetation, Image classification, Data modeling, Statistical analysis, Error analysis, Matrices, Chlorine, Neural networks, Remote sensing

Read Abstract +

Proceedings Article | 13 October 1998 Paper

Parallel genetic algorithm for the design of neural networks: an application to the classification of remotely sensed data

Sebastiano Stramaglia, Giuseppe Satalino, A. Sternieri, P. Anelli, Palma Blonda, Guido Pasquariello

Proceedings Volume 3455, (1998) https://doi.org/10.1117/12.326727

KEYWORDS: Genetic algorithms, Neural networks, Image classification, Evolutionary algorithms, Landsat, Protactinium, Lead, Neurons, Data acquisition, Earth observing sensors

Read Abstract +

Proceedings Article | 13 October 1997 Paper

Experimental comparison of FOSART and FLVQ in a remotely sensed image classification task

Palma Blonda, Andrea Baraldi, G. Bafunno, Giuseppe Satalino, G. Ria

Proceedings Volume 3165, (1997) https://doi.org/10.1117/12.290270

KEYWORDS: Neurons, Image classification, Fuzzy logic

Read Abstract +

Proceedings Article | 14 June 1996 Paper

Fuzzy neural-network-based segmentation of multispectral magnetic-resonance brain images

Palma Blonda, A. Bennardo, Giuseppe Satalino, Guido Pasquariello, Roberto De Blasi, D. Milella

Proceedings Volume 2761, (1996) https://doi.org/10.1117/12.243248

KEYWORDS: Image segmentation, Tissues, Brain, Magnetic resonance imaging, Neuroimaging, Neurons, Multispectral imaging, Brain imaging, Magnetism, Image analysis

Read Abstract +

Proceedings Article | 14 June 1996 Paper

Application of the fuzzy Kohonen clustering network to remote-sensed data processing

Palma Blonda, A. Bennardo, Guido Pasquariello, Giuseppe Satalino, Vincenza la Forgia

Proceedings Volume 2761, (1996) https://doi.org/10.1117/12.243245

KEYWORDS: Classification systems, Fuzzy logic, Neural networks, Neurons, Data processing, Feature extraction, Data acquisition, Data modeling, Information operations, Radon

Read Abstract +

SPIE Journal Paper | 1 February 1996

Feature extraction and pattern classification of remote sensing data by a modular neural system

Palma Blonda, Vincenza la Forgia, Guido Pasquariello, Giuseppe Satalino

OE, Vol. 35, Issue 02, (February 1996) https://doi.org/10.1117/12.10.1117/1.600898

KEYWORDS: Classification systems, Neurons, Image classification, Feature extraction, Remote sensing, Neural networks, Data compression, Machine learning, Computer architecture, Computing systems

Proceedings Article | 30 December 1994 Paper

Feature extraction and pattern classification for remotely sensed data analysis by a modular neural system

Palma Blonda, Vincenza la Forgia, Guido Pasquariello, Giuseppe Satalino

Proceedings Volume 2315, (1994) https://doi.org/10.1117/12.196743

KEYWORDS: Image classification, Neural networks, Classification systems, Binary data, Data compression, Data modeling, Feature extraction, Data analysis, Neurons, Image compression

Read Abstract +

Showing 5 of 12 publications

Conference Committee Involvement (5)

Image and Signal Processing for Remote Sensing

18 September 2007 | Florence, Italy

Image and Signal Processing for Remote Sensing XII

13 September 2006 | Stockholm, Sweden

Image and Signal Processing for Remote Sensing XI

20 September 2005 | Bruges, Belgium

Image and Signal Processing for Remote Sensing X

13 September 2004 | Maspalomas, Canary Islands, Spain

Image and Signal Processing for Remote Sensing IX

9 September 2003 | Barcelona, Spain

Keywords/Phrases

Search In:

Publication Years