Claudio Persello

Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images

This paper investigates different batch-mode active-learning (AL) techniques for the classification of remote sensing (RS) images with support vector machines. This is done by generalizing to multiclass problem 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 iteration of the AL process. Moreover, we propose a novel query function that is based on a kernel-clustering technique for assessing the diversity of samples and a new strategy for selecting the most informative representative sample from each cluster. The investigated and proposed techniques are theoretically and experimentally compared with state-of-the-art methods adopted for RS applications. This is accomplished by considering very high resolution multispectral and hyperspectral images. By this comparison, we observed that the proposed method resulted in better accuracy with respect to other investigated and state-of-the art methods on both the considered data sets. Furthermore, we derived some guidelines on the design of AL systems for the classification of different types of RS images.

A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples

This paper presents a novel context-sensitive semisupervised support vector machine (CS4VM) classifier, which is aimed at addressing classification problems where the available training set is not fully reliable, i.e., some labeled samples may be associated to the wrong information class (mislabeled patterns). Unlike standard context-sensitive methods, the proposed CS4VM classifier exploits the contextual information of the pixels belonging to the neighborhood system of each training sample in the learning phase to improve the robustness to possible mislabeled training patterns. This is achieved according to both the design of a semisupervised procedure and the definition of a novel contextual term in the cost function associated with the learning of the classifier. In order to assess the effectiveness of the proposed CS4VM and to understand the impact of the addressed problem in real applications, we also present an extensive experimental analysis carried out on training sets that include different percentages of mislabeled patterns having different distributions on the classes. In the analysis, we also study the robustness to mislabeled training patterns of some widely used supervised and semisupervised classification algorithms (i.e., conventional support vector machine (SVM), progressive semisupervised SVM, maximum likelihood, and k-nearest neighbor). Results obtained on a very high resolution image and on a medium resolution image confirm both the robustness and the effectiveness of the proposed CS4VM with respect to standard classification algorithms and allow us to derive interesting conclusions on the effects of mislabeled patterns on different classifiers.

A Novel Approach to the Selection of Spatially Invariant Features for the Classification of Hyperspectral Images With Improved Generalization Capability

This paper presents a novel approach to feature selection for the classification of hyperspectral images. The proposed approach aims at selecting a subset of the original set of features that exhibits at the same time high capability to discriminate among the considered classes and high invariance in the spatial domain of the investigated scene. This approach results in a more robust classification system with improved generalization properties with respect to standard feature-selection methods. The feature selection is accomplished by defining a multiobjective criterion function made up of two terms: (1) a term that measures the class separability and (2) a term that evaluates the spatial invariance of the selected features. In order to assess the spatial invariance of the feature subset, we propose both a supervised method (which assumes that training samples acquired in two or more spatially disjoint areas are available) and a semisupervised method (which requires only a standard training set acquired in a single area of the scene and takes advantage of unlabeled samples selected in portions of the scene spatially disjoint from the training set). The choice for the supervised or semisupervised method depends on the available reference data. The multiobjective problem is solved by an evolutionary algorithm that estimates the set of Pareto-optimal solutions. Experiments carried out on a hyperspectral image acquired by the Hyperion sensor on a complex area confirmed the effectiveness of the proposed approach.

Active and Semisupervised Learning for the Classification of Remote Sensing Images

This paper aims at analyzing and comparing active learning (AL) and semisupervised learning (SSL) methods for the classification of remote sensing (RS) images. We present a literature review of the two learning paradigms and compare them theoretically and experimentally when addressing classification problems characterized by few training samples (w.r.t. the number of features) and affected by sample selection bias. Commonalities and differences are highlighted in the context of a conceptual framework used to describe the workflow of the two approaches. We point out advantages and disadvantages of the two approaches, delineating the boundary conditions on the applicability of the two paradigms with respect to both the amount and the quality of available training samples. Moreover, we investigate the integration of concepts that are in common between the two learning paradigms for improving state-of-the-art techniques and combining AL and SSL in order to jointly leverage the advantages of both approaches. In this framework, we propose a novel SSL algorithm that improves the progressive semisupervised support vector machine by integrating concepts that are usually considered in AL methods. We performed several experiments considering both synthetic and real multispectral and hyperspectral RS data, defining different classification problems starting from different initial training sets. The experiments are carried out considering classification methods based on support vector machines.

A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Images

This paper presents a novel protocol for the accuracy assessment of the thematic maps obtained by the classification of very high resolution images. As the thematic accuracy alone is not sufficient to adequately characterize the geometrical properties of high-resolution classification maps, we propose a protocol that is based on the analysis of two families of indices: 1) the traditional thematic accuracy indices and 2) a set of novel geometric indices that model different geometric properties of the objects recognized in the map. In this context, we present a set of indices that characterize five different types of geometric errors in the classification map: 1) oversegmentation; 2) undersegmentation; 3) edge location; 4) shape distortion; and 5) fragmentation. Moreover, we propose a new approach for tuning the free parameters of supervised classifiers on the basis of a multiobjective criterion function that aims at selecting the parameter values that result in the classification map that jointly optimize thematic and geometric error indices. Experimental results obtained on QuickBird images show the effectiveness of the proposed protocol in selecting classification maps characterized by a better tradeoff between thematic and geometric accuracies than standard procedures based only on thematic accuracy measures. In addition, results obtained with support vector machine classifiers confirm the effectiveness of the proposed multiobjective technique for the selection of free-parameter values for the classification algorithm.

Active Learning for Domain Adaptation in the Supervised Classification of Remote Sensing Images

This paper presents a novel technique for addressing domain adaptation (DA) problems with active learning (AL) in the classification of remote sensing images. DA models the important problem of adapting a supervised classifier trained on a given image (source domain) to the classification of another similar but not identical image (target domain) acquired on a different area. The main idea of the proposed approach is iteratively labeling and adding to the training set the minimum number of the most informative samples from the target domain, while removing the source-domain samples that do not fit with the distributions of the classes in the target domain. In this way, the classification system exploits already available information, i.e., the labeled samples of source domain, in order to minimize the number of target domain samples to be labeled, thus reducing the cost associated to the definition of the training set for the classification of the target domain. In addition, we define a convergence criterion that allows the technique to stop the iterative AL process on the target domain without relying on the availability of a test set for it. This is an important contribution, as in operational applications, it is not realistic to assume that a test set for the target domain is available. Experimental results obtained in the classification of very high resolution and hyperspectral images confirm the effectiveness of the proposed technique.

Interactive Domain Adaptation for the Classification of Remote Sensing Images Using Active Learning

This letter presents a novel interactive domain-adaptation technique based on active learning for the classification of remote sensing (RS) images. The proposed method aims at adapting the supervised classifier trained on a given RS source image to make it suitable for classifying a different but related target image. The two images can be acquired in different locations and/or at different times. The proposed approach iteratively selects the most informative samples of the target image to be labeled by the user and included in the training set, whereas the source image samples are reweighted or possibly removed from the training set on the basis of their disagreement with the target image classification problem. This way, the consistent information available from the source image can be effectively exploited for the classification of the target image and for guiding the selection of new samples to be labeled, whereas the inconsistent information is automatically detected and removed. This approach can significantly reduce the number of new labeled samples to be collected from the target image. Experimental results on both a multispectral very high resolution and a hyperspectral data set confirm the effectiveness of the proposed method.

Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning

This paper presents a kernel-based feature selection method for the classification of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant (discriminant) for the considered classification problem, i.e., preserve the functional relationship between input and output variables, and 2) invariant (stable) across different domains, i.e., minimize the data-set shift between the source and the target domains. Domains can be associated with hyperspectral images collected either on different geographical areas or on the same area at different times. We propose a novel measure of data-set shift for evaluating the domain stability, which computes the distance of the conditional distributions between the source and target domains in a reproducing kernel Hilbert space. Such a measure is defined on the basis of the kernel embeddings of the conditional distributions resulting in a nonparametric approach that does not require estimating the distribution of the classes. The adopted search strategy is based on a multiobjective optimization algorithm, which optimizes the two terms of the criterion function for the estimation of the Pareto-optimal solutions. This results in an effective approach of performing feature selection in a transfer learning setting. The experimental results obtained on two hyperspectral images show the effectiveness of the proposed method in selecting features with high generalization capabilities.

Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification

Active learning typically aims at minimizing the number of labeled samples to be included in the training set to reach a certain level of classification accuracy. Standard methods do not usually take into account the real annotation procedures and implicitly assume that all samples require the same effort to be labeled. Here, we consider the case where the cost associated with the annotation of a given sample depends on the previously labeled samples. In general, this is the case when annotating a queried sample is an action that changes the state of a dynamic system, and the cost is a function of the state of the system. In order to minimize the total annotation cost, the active sample selection problem is addressed in the framework of a Markov decision process, which allows one to plan the next labeling action on the basis of an expected long-term cumulative reward. This framework allows us to address the problem of optimizing the collection of labeled samples by field surveys for the classification of remote sensing data. The proposed method is applied to the ground sample collection for tree species classification using airborne hyperspectral images. Experiments carried out in the context of a real case study on forest inventory show the effectiveness of the proposed method.

A novel active learning strategy for domain adaptation in the classification of remote sensing images

We present a novel technique for addressing domain adaptation problems in the classification of remote sensing images with active learning. Domain adaptation is the important problem of adapting a supervised classifier trained on a given image (source domain) to the classification of another similar (but not identical) image (target domain) acquired on a different area, or on the same area at a different time. The main idea of the proposed approach is to iteratively labeling and adding to the training set the minimum number of the most informative samples from target domain, while removing the source-domain samples that does not fit with the distributions of the classes in the target domain. In this way, the classification system exploits already available information, i.e., the labeled samples of source domain, in order to minimize the number of target domain samples to be labeled, thus reducing the cost associated to the definition of the training set for the classification of the target domain. Experimental results obtained in the classification of a hyperspectral image confirm the effectiveness of the proposed technique.