A Multiple Cascade-Classifier System for a Robust and Partially Unsupervised Updating of Land-Cover Maps

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 classification process under the aforementioned critical constraint. Both a parametric maximum-likelihood classification approach and a non-parametric radial basis function (RBF) neural-network classification approach are used as basic methods for the development of partially unsupervised cascade classifiers. In addition, in order to generate an effective ensemble of classification algorithms, hybrid maximum-likelihood and RBF neural network cascade classifiers are defined by exploiting the peculiarities of the cascade-classification methodology. The results yielded by the different classifiers are combined by using standard unsupervised combination strategies. This allows the definition of a robust and accurate partially unsupervised classification system capable of analyzing a wide typology of remote-sensing data (e.g., images acquired by passive sensors, SAR images, multisensor and multisource data). Experimental results obtained on a real multitemporal and multisource data set confirm the effectiveness of the proposed system

Dissimilarity-based Ensembles for Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation determined by the number of training bags, while the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. In this paper a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide guidelines for using such an ensemble, and show state-of-the-art performances on a range of multiple instance learning problems.Comment: Submitted to IEEE Transactions on Neural Networks and Learning Systems, Special Issue on Learning in Non-(geo)metric Space

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Abstract: Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy-assessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and a quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizingmaps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results

Tackling Uncertainties and Errors in the Satellite Monitoring of Forest Cover Change

This study aims at improving the reliability of automatic forest change detection. Forest change detection is of vital importance for understanding global land cover as well as the carbon cycle. Remote sensing and machine learning have been widely adopted for such studies with increasing degrees of success. However, contemporary global studies still suffer from lower-than-satisfactory accuracies and robustness problems whose causes were largely unknown. Global geographical observations are complex, as a result of the hidden interweaving geographical processes. Is it possible that some geographical complexities were not expected in contemporary machine learning? Could they cause uncertainties and errors when contemporary machine learning theories are applied for remote sensing? This dissertation adopts the philosophy of error elimination. We start by explaining the mathematical origins of possible geographic uncertainties and errors in chapter two. Uncertainties are unavoidable but might be mitigated. Errors are hidden but might be found and corrected. Then in chapter three, experiments are specifically designed to assess whether or not the contemporary machine learning theories can handle these geographic uncertainties and errors. In chapter four, we identify an unreported systemic error source: the proportion distribution of classes in the training set. A subsequent Bayesian Optimal solution is designed to combine Support Vector Machine and Maximum Likelihood. Finally, in chapter five, we demonstrate how this type of error is widespread not just in classification algorithms, but also embedded in the conceptual definition of geographic classes before the classification. In chapter six, the sources of errors and uncertainties and their solutions are summarized, with theoretical implications for future studies. The most important finding is that, how we design a classification largely pre-determines what we eventually get out of it. This applies for many contemporary popular classifiers including various types of neural nets, decision tree, and support vector machine. This is a cause of the so-called overfitting problem in contemporary machine learning. Therefore, we propose that the emphasis of classification work be shifted to the planning stage before the actual classification. Geography should not just be the analysis of collected observations, but also about the planning of observation collection. This is where geography, machine learning, and survey statistics meet

Optimized classification predictions with a new index combining machine learning algorithms

Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search

Contributions for the improvement of specific class mapping

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe analysis of remotely sensed imagery has become a fundamental task for many environmental centred activities, not just scientific but also management related. In particular, the use of land cover maps depicting a particular study site is an integral part of many research projects, as they are not just a fundamental variable in environmental models but also base information supporting policy decisions. Land cover mapping assisted by supervised classification is today a staple tool of any analyst processing remotely sensed data, insomuch as these techniques allow users to map entire sites of interest in a omprehensive way. Many remote sensing projects are usually interested in a small number of land cover classes present in a study area and not in all classes that make-up the landscape. When focus is on a particular sub-set of classes of interest, conventional supervised classification may be sub-optimal for the discrimination of these specific target classes. The process of producing a non-exhaustive map, that is depicting only the classes of interest for the user, is called specific class mapping. This is the topic of this dissertation. Here, specific class mapping is examined to understand its origins, developments, adoption and current limitations. The main research goal is then to contribute for the understanding and improvement of this topic, while presenting its main constrains in a clear way and proposing enhanced methods at the reach of the non-expert user. In detail, this study starts by analysing the definition of specific class mapping and why the conventional multi-class supervised classification process may yield sub-optimal outcomes. Attention then is turn to the previous works that have tackled this problem. From here a synthesis is made, categorising and characterising previous methodologies. Its then learnt that the methodologies tackling specific class mapping fall under two broad categories, the binarisation approaches and the singe-class approaches, and that both types are not without problems. This is the starting point of the development component of this dissertation that branches out in three research lines. First, cost-sensitive learning is utilised to improve specific class mapping. In previous studies it was shown that it may be susceptible to data imbalance problems present in the training data set, since the classes of interest are often a small part of the training set. As a result the classification may be biased towards the largest classes and, thus, be sub-optimal for the discrimination of the classes of interest. Here cost-sensitive learning is used to balance the training data set to minimise the effects of data imbalance. In this approach errors committed in the minority class are treated as being costlier than errors committed in the majority class. Cost-sensitive approaches are typically implemented by weighting training data points accordingly to their importance to the analysis. By shifting the weight of the data set from the majority class to the minority class, the user is capable to inform the learning process that training data points in the minority class are as critical as the points in the majority class. The results of this study indicate that this simple approach is capable to improve the process of specific class mapping by increasing the accuracy to which the classes of interest are discriminated. Second, the combined use single-class classifiers for specific class mapping is explored. Supervised algorithms for single-class classification are particularly attractive due to its reduced training requirements. Unlike other methods where all classes present in the study site regardless of its relevance for the particular objective to the users, single-class classifiers rely exclusively on the training of the class of interest. However, these methods can only solve specific classification problems with one class of interest. If more classes are important, those methods cannot be directly utilised. Here is proposed three combining methodologies to combine single-class classifiers to map subsets of land cover classes. The results indicate that an intelligent combination of single-class classifiers can be used to achieve accurate results, statistically noninferior to the standard multi-class classification, without the need of an exhaustive training set, saving resources that can be allocated to other steps of the data analysis process. Third, the combined use of cost-sensitive and semi-supervised learning to improve specific class mapping is explored. A limitation of the specific class binary approaches is that they still require training data from secondary classes, and that may be costly. On the other hand, a limitation of the specific class single-class approaches is that, while requiring only training data from the specific classes of interest, this method tend to overestimate the extension of the classes of interest. This is because the classifier is trained without information about the negative part of the classification space. A way to overcome this is with semi-supervised learning, where the data points for the negative class are randomly sampled from the classification space. However that may include false negatives. To overcome this difficult, cost-sensitive learning is utilised to mitigate the effect of these potentially misclassified data points. Cost weights were here defined using an exponential model that assign more weight to the negative data points that are more likely to be correctly labelled and less to the points that are more likely to be mislabelled. The results show that accuracy achieved with the proposed method is statistically non-inferior to that achieved with standard binary classification requiring however much less training effort