Sebastiano B. Serpico

Image fusion techniques for remote sensing applications

Image fusion refers to the acquisition, processing andsynergistic combination of information providedby various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the synthetic aperture radar (SAR) interferometry, where a pair of antennas are usedto obtain an elevation map of the observedscene; the secondone refers to the fusion of multisensor andmultitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the thirdone presents a processor to fuse multifrequency, multipolarization andmutiresolution SAR images, basedon wavelet transform andmultiscale Kalman filter (MKF). Each stud y case presents also the results achievedby the proposedtechniques appliedto real d ata. � 2002 Elsevier Science B.V. All rights reserved.

A new search algorithm for feature selection in hyperspectral remote sensing images

A new suboptimal search strategy suitable for feature selection in very high-dimensional remote sensing images (e.g., those acquired by hyperspectral sensors) is proposed. Each solution of the feature selection problem is represented as a binary string that indicates which features are selected and which are disregarded. In turn, each binary string corresponds to a point of a multidimensional binary space. Given a criterion function to evaluate the effectiveness of a selected solution, the proposed strategy is based on the search for constrained local extremes of such a function in the above-defined binary space. In particular, two different algorithms are presented that explore the space of solutions in different ways. These algorithms are compared with the classical sequential forward selection and sequential forward floating selection suboptimal techniques, using hyperspectral remote sensing images (acquired by the airborne visible/infrared imaging spectrometer [AVIRIS] sensor) as a data set. Experimental results point out the effectiveness of both algorithms, which can be regarded as valid alternatives to classical methods, as they allow interesting tradeoffs between the qualities of selected feature subsets and computational cost.

An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection

The problem of extending the Jeffreys-Matusita distance to multiclass cases for feature-selection purposes is addressed and a solution equivalent to the Bhattacharyya bound is presented. This extension is compared with the widely used weighted average Jeffreys-Matusita distance both by examining the respective formulae and by experimenting on an optical remote-sensing data set.

Combining Support Vector Machines and Markov Random Fields in an Integrated Framework for Contextual Image Classification

In the framework of remote-sensing image classification, support vector machines (SVMs) have lately been receiving substantial attention due to their accurate results in many applications as well as their remarkable generalization capability even with high-dimensional input data. However, SVM classifiers are intrinsically noncontextual, which represents an important limitation in image classification. In this paper, a novel and rigorous framework, which integrates SVMs and Markov random field models in a unique formulation for spatial contextual classification, is proposed. The developed contextual generalization of SVMs, is obtained by analytically relating the Markovian minimum-energy criterion to the application of an SVM in a suitably transformed space. Furthermore, as a second contribution, a novel contextual classifier is developed in the proposed general framework. Two specific algorithms, based on the Ho–Kashyap and Powell numerical procedures, are combined with this classifier to automate the estimation of its parameters. Experiments are carried out with hyperspectral, multichannel synthetic aperture radar, and multispectral high-resolution images and the behavior of the method as a function of the training-set size is assessed.

Partially Supervised classification of remote sensing images through SVM-based probability density estimation

A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all the thematic classes that are present in the considered dataset. However, the ground-truth map representing that prior knowledge usually does not really describe all the land-cover typologies in the image, and the generation of a complete training set often represents a time-consuming, difficult and expensive task. This problem affects the performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is described that allows the identification of samples drawn from unknown classes through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density functions and on a recursive procedure to generate prior probability estimates for known and unknown classes. In the experiments, both a synthetic dataset and two real datasets were used.

Land-Cover Mapping by Markov Modeling of Spatial–Contextual Information in Very-High-Resolution Remote Sensing Images

Markov models represent a wide and general family of stochastic models for the temporal and spatial dependence properties associated to 1-D and multidimensional random sequences or random fields. Their applications range over a wide variety of subareas of the information and communication technology (ICT) field, including networking, automation, speech processing, genomic-sequence analysis, or image processing. Focusing on the applicative problem of land-cover mapping from very-high-resolution (VHR) remote sensing images, which is a relevant problem in many applications of environmental monitoring and natural resource exploitation, Markov models convey a great potential, thanks to their capability to effectively describe and incorporate the spatial information associated with image data into an image-classification process. In this framework, the main ideas and previous work about Markov modeling for VHR image classification will be recalled in this paper and processing results obtained through recent methods proposed by the authors will be discussed.

Partially supervised classification of remote sensing images using SVM-based probability density estimation

A general problem of supervised remotely. sensed image classification assumes prior knowledge to be available for all thematic classes that are present in the considered data set. However, the ground truth map representing this prior knowledge usually does not really, describe all the land cover typologies in the image and the generation of a complete training set represents a time-consuming, difficult and expensive task. This problem may play a relevant role in remote sensing data analysis, since it affects the classification performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is proposed, which allows the identification of samples drawn from unknown classes, through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density, functions and on a recursive procedure to generate prior probabilities estimates for both known and unknown classes. For experimental purposes, both a synthetic data set and two real data sets are employed.

Unsupervised Change Detection From Multichannel SAR Data by Markovian Data Fusion

In applications related to environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a great potential, owing both to their insensitivity to atmospheric and Sun-illumination conditions and to the improved discrimination capability they may provide as compared with single-channel SAR. However, exploiting this potential requires accurate and automatic techniques to generate change maps from (multichannel) SAR images acquired over the same geographic region in different polarizations or at different frequencies at different times. In this paper, a contextual unsupervised change-detection technique (based on a data-fusion approach) is proposed for two-date multichannel SAR images. Each SAR channel is modeled as a distinct information source, and a Markovian approach to data fusion is adopted. A Markov random field model is introduced that combines together the information conveyed by each SAR channel and the spatial contextual information concerning the correlation among neighboring pixels and formulated by using ldquoenergy functions.rdquo In order to address the task of the estimation of the model parameters, the expectation-maximization algorithm is combined with the recently proposed ldquomethod of log-cumulants.rdquo The proposed technique was experimentally validated with semisimulated multipolarization and multifrequency data and with real SIR-C/XSAR images.

An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images

Abstract An experimental analysis of the use of different neural models for the supervised classification of multisensor remote-sensing data is presented. Tbree types of neural classifiers are considered: the Multilayer Perceptron, a kind of Structured Neural Network, proposed by the authors, that allows the interpretation of the network operation, and a Probabilistic Neural Network. Furthermore, the k -nearest neighbour statistical classifier is also considered in order to evaluate the validity of the aforementioned neural networks, as compared with that of classical statistical methods. The results provided by the above classifiers are compared.

A technique for feature selection in multiclass problems

One of the main phases in the development of a system for the classification of remote sensing images is the definition of an effective set of features to be given as input to the classifier. In particular, it is often useful to reduce the number of features available, while saving the possibility to discriminate among the different land-cover classes to be recognized. This paper addresses this topic with reference to applications that involve more than two land-cover classes (multiclass problems). Several criteria proposed in the remote sensing literature are considered and compared with one another and with the criterion presented by the authors. Such a criterion, unlike those usually adopted for multiclass problems, is related to an upper bound to the error probability of the Bayes classifier. As the objective of feature selection is generally to identify a reduced set of features that minimize the errors of the classifier, the aforementioned property is very important because it allows one to select features by taking into account their effects on classification errors. Experiments on two remote sensing datasets are described and discussed. These experiments confirm the effectiveness of the proposed criterion, which performs slightly better than all the others considered in the paper. In addition, the results obtained provide useful information about the behaviour of different classical criteria when applied in multiclass cases.