Commentary on Section 264 of ITAA 1936: commissioner may require information and evidence
Dabner, Justin
2007-01-01
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29 records were found.
The recent technological developments in remote-sensing sensors and satellites (e.g., the increased spatial and spectral resolutions of sensors, the increased rivisitation time of satellites) offer the possibility of addressing new applications related to environmental monitoring and natural-resource management. In particular, applications connected with the analysis of multitemporal remote-sensing images are becoming more and more important, also in relation to the increased awareness of politicians of the necessity for a regular and efficient control of the environment. This chapter deals with a key issue in multitemporal data analysis, namely, the automatic detection of changes in pairs of images acquired in the same geographical area at different times. In particular, unsupervised change-detection methods (i.e., methods that do not...
A system for a regular updating of land-cover maps is proposed that is based on the use of multitemporal remote-sensing images. Such a system is able to face the updating problem under the realistic but critical constraint that, for the image to be classified (i.e., the most recent of the considered multitemporal data set), no ground truth information is available. The system is composed of an ensemble of partially unsupervised classifiers integrated in a multiple classifier architecture. Each classifier of the ensemble exhibits the following novel peculiarities: i) it is developed in the framework of the cascade-classification approach to exploit the temporal correlation existing between images acquired at different times in the considered area; ii) it is based on a partially unsupervised methodology capable to accomplish the classifi...
This paper addresses unsupervised change detection by proposing a proper framework for a formal definition and a theoretical study of the change vector analysis (CVA) technique. This framework, which is based on the representation of the CVA in polar coordinates, aims at: i) introducing a set of formal definitions in the polar domain (which are linked to the properties of the data) for a better general description (and thus understanding) of the information present in spectral change vectors; ii) analyzing from a theoretical point of view the distributions of changed and unchanged pixels in the polar domain (also according to possible simplifying assumptions); iii) driving the implementation of proper pre-processing procedures to be applied to multitemporal images on the basis of the results of the theoretical study on the distribution...
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 selectio...
A partially unsupervised approach to the classification of multitemporal remote-sensing images is presented. Such an approach allows the automatic classification of a remote-sensing image for which training data are not available, drawing on the information derived from an image acquired in the same area at a previous time. In particular, the proposed technique is based on a cascade classifier approach and on a specific formulation of the expectation-maximization (EM) algorithm used for the unsupervised estimation of the statistical parameters of the image to be classified. The results of experiments carried out on a multitemporal data set confirm the validity of the proposed approach.
In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semi-parametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov Random Field (MRF) approach used to model the spatial-contextual information contained in ...
summary:In this paper, feature selection in multiclass cases for classification of remote-sensing images is addressed. A criterion based on a simple upper bound to the error probability of the Bayes classifier for the minimum error is proposed. This criterion has the advantage of selecting features having a link with the error probability with a low computational load. Experiments have been carried out in order to compare the performances provided by the proposed criterion with the ones of some of the widely used feature-selection criteria presented in the remote-sensing literature. These experiments confirm the effectiveness of the proposed criterion, which performs slightly better than all the others considered in the paper.
In this paper, we propose a classification system based on a multiple-classifier architecture, which is aimed at updating land-cover maps by using multisensor and/or multisource remote-sensing images. The proposed system is composed of an ensemble of classifiers that, once trained in a supervised way on a specific image of a given area, can be retrained in an unsupervised way to classify a new image of the considered site. In this context, two techniques are presented for the unsupervised updating of the parameters of a maximum-likelihood (ML) classifier and a radial basis function (RBF) neural-network classifier, on the basis of the distribution of the new image to be classified. Experimental results carried out on a multitemporal and multisource remote-sensing data set confirm the effectiveness of the proposed system.
This paper investigates different batch mode active learning techniques for the classification of remote sensing (RS) images with support vector machines (SVMs). This is done by generalizing to multiclass problems techniques defined for binary classifiers. The investigated techniques exploit different query functions, which are based on the evaluation of two criteria: uncertainty and diversity. The uncertainty criterion is associated to the confidence of the supervised algorithm in correctly classifying the considered sample, while the diversity criterion aims at selecting a set of unlabeled samples that are as more diverse (distant one another) as possible, thus reducing the redundancy among the selected samples. The combination of the two criteria results in the selection of the potentially most informative set of samples at each ite...
