Begüm Demir

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.

Hyperspectral Image Classification Using Relevance Vector Machines

This letter presents a hyperspectral image classification method based on relevance vector machines (RVMs). Support vector machine (SVM)-based approaches have been recently proposed for hyperspectral image classification and have raised important interest. In this letter, it is genuinely proposed to use an RVM-based approach for the classification of hyperspectral images. It is shown that approximately the same classification accuracy is obtained using RVM-based classification, with a significantly smaller relevance vector rate and, therefore, much faster testing time, compared with SVM-based classification. This feature makes the RVM-based hyperspectral classification approach more suitable for applications that require low complexity and, possibly, real-time classification.

Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach

This paper proposes a novel change-detection-driven transfer learning (TL) approach to update land-cover maps by classifying remote-sensing images acquired on the same area at different times (i.e., image time series). The proposed approach requires that a reliable training set is available only for one of the images (i.e., the source domain) in the time series whereas it is not for another image to be classified (i.e., the target domain). Unlike other literature TL methods, no additional assumptions on either the similarity between class distributions or the presence of the same set of land-cover classes in the two domains are required. The proposed method aims at defining a reliable training set for the target domain, taking advantage of the already available knowledge on the source domain. This is done by applying an unsupervised-change-detection method to target and source domains and transferring class labels of detected unchanged training samples from the source to the target domain to initialize the target-domain training set. The training set is then optimized by a properly defined novel active learning (AL) procedure. At the early iterations of AL, priority in labeling is given to samples detected as being changed, whereas in the remaining ones, the most informative samples are selected from changed and unchanged unlabeled samples. Finally, the target image is classified. Experimental results show that transferring the class labels from the source domain to the target domain provides a reliable initial training set and that the priority rule for AL results in a fast convergence to the desired accuracy with respect to Standard AL.

A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval

Conventional relevance feedback (RF) schemes improve the performance of content-based image retrieval (CBIR) requiring the user to annotate a large number of images. To reduce the labeling effort of the user, this paper presents a novel active learning (AL) method to drive RF for retrieving remote sensing images from large archives in the framework of the support vector machine classifier. The proposed AL method is specifically designed for CBIR and defines an effective and as small as possible set of relevant and irrelevant images with regard to a general query image by jointly evaluating three criteria: uncertainty; diversity; and density of images in the archive. The uncertainty and diversity criteria aim at selecting the most informative images in the archive, whereas the density criterion goal is to choose the images that are representative of the underlying distribution of data in the archive. The proposed AL method assesses jointly the three criteria based on two successive steps. In the first step, the most uncertain (i.e., ambiguous) images are selected from the archive on the basis of the margin sampling strategy. In the second step, the images that are both diverse (i.e., distant) to each other and associated to the high-density regions of the image feature space in the archive are chosen from the most uncertain images. This step is achieved by a novel clustering-based strategy. The proposed AL method for driving the RF contributes to mitigate problems of unbalanced and biased set of relevant and irrelevant images. Experimental results show the effectiveness of the proposed AL method.

A Low-Complexity Approach for the Color Display of Hyperspectral Remote-Sensing Images Using One-Bit-Transform-Based Band Selection

This paper presents a new approach for the color display of hyperspectral images. It is proposed to use the one-bit transform (1BT) of hyperspectral image bands to select three suitable bands for red, green, and blue (RGB) display. The proposed approach has low complexity and is very suitable for hardware implementation. A dedicated hardware architecture that computes the transitions in the 1BT of hyperspectral image bands to determine bands that contain more information and the corresponding field-programmable gate array implementation of the proposed architecture are presented. In the proposed approach, less-structured bands are initially eliminated using the total number of transitions in the 1BT of hyperspectral image bands. Then, three suitable bands are selected from within this remaining set of well-structured bands for RGB color display. The proposed approach provides a new method for facilitating the color display of hyperspectral images, which has very low complexity.

Clustering-Based Extraction of Border Training Patterns for Accurate SVM Classification of Hyperspectral Images

This letter presents an accurate support vector machine (SVM)-based hyperspectral image classification algorithm, which uses border training patterns that are close to the separating hyperplane. Border training patterns are obtained in two consecutive steps. In the first step, clustering is performed to training data of each class, and cluster centers are taken as initial training data for SVM. In the second step, the reduced-size training data composed of cluster centers are used in SVM training, and cluster centers obtained as support vectors at this step are regarded to be located close to the hyperplane border. Original training samples are contained in clusters for which the cluster centers are obtained to be close to the hyperplane border and the corresponding cluster centers are then together assigned as border training patterns. These border training patterns are then used in the training of the SVM classifier. Experimental results show that it is possible to significantly increase the classification accuracy of SVM using border training patterns obtained with the proposed approach.

Hashing-Based Scalable Remote Sensing Image Search and Retrieval in Large Archives

Large-scale remote sensing (RS) image search and retrieval have recently attracted great attention, due to the rapid evolution of satellite systems, that results in a sharp growing of image archives. An exhaustive search through linear scan from such archives is time demanding and not scalable in operational applications. To overcome such a problem, this paper introduces hashing-based approximate nearest neighbor search for fast and accurate image search and retrieval in large RS data archives. The hashing aims at mapping high-dimensional image feature vectors into compact binary hash codes, which are indexed into a hash table that enables real-time search and accurate retrieval. Such binary hash codes can also significantly reduce the amount of memory required for storing the RS images in the auxiliary archives. In particular, in this paper, we introduce in RS two kernel-based nonlinear hashing methods. The first hashing method defines hash functions in the kernel space by using only unlabeled images, while the second method leverages on the semantic similarity extracted by annotated images to describe much distinctive hash functions in the kernel space. The effectiveness of considered hashing methods is analyzed in terms of RS image retrieval accuracy and retrieval time. Experiments carried out on an archive of aerial images point out that the presented hashing methods are much faster, while keeping a similar (or even higher) retrieval accuracy, than those typically used in RS, which exploit an exact nearest neighbor search.

A multiple criteria active learning method for support vector regression

Abstract This paper presents a novel active learning method developed in the framework of e-insensitive support vector regression (SVR) for the solution of regression problems with small size initial training data. The proposed active learning method selects iteratively the most informative as well as representative unlabeled samples to be included in the training set by jointly evaluating three criteria: (i) relevancy, (ii) diversity, and (iii) density of samples. All three criteria are implemented according to the SVR properties and are applied in two clustering-based consecutive steps. In the first step, a novel measure to select the most relevant samples that have high probability to be located either outside or on the boundary of the e-tube of SVR is defined. To this end, initially a clustering method is applied to all unlabeled samples together with the training samples that are inside the e-tube (those that are not support vectors, i.e., non-SVs); then the clusters with non-SVs are eliminated. The unlabeled samples in the remaining clusters are considered as the most relevant patterns. In the second step, a novel measure to select diverse samples among the relevant patterns from the high density regions in the feature space is defined to better model the SVR learning function. To this end, initially clusters with the highest density of samples are chosen to identify the highest density regions in the feature space. Then, the sample from each selected cluster that is associated with the portion of feature space having the highest density (i.e., the most representative of the underlying distribution of samples contained in the related cluster) is selected to be included in the training set. In this way diverse samples taken from high density regions are efficiently identified. Experimental results obtained on four different data sets show the robustness of the proposed technique particularly when a small-size initial training set are available.

Classification of Time Series of Multispectral Images With Limited Training Data

Image classification usually requires the availability of reliable reference data collected for the considered image to train supervised classifiers. Unfortunately when time series of images are considered, this is seldom possible because of the costs associated with reference data collection. In most of the applications it is realistic to have reference data available for one or few images of a time series acquired on the area of interest. In this paper, we present a novel system for automatically classifying image time series that takes advantage of image(s) with an associated reference information (i.e., the source domain) to classify image(s) for which reference information is not available (i.e., the target domain). The proposed system exploits the already available knowledge on the source domain and, when possible, integrates it with a minimum amount of new labeled data for the target domain. In addition, it is able to handle possible significant differences between statistical distributions of the source and target domains. Here, the method is presented in the context of classification of remote sensing image time series, where ground reference data collection is a highly critical and demanding task. Experimental results show the effectiveness of the proposed technique. The method can work on multimodal (e.g., multispectral) images.

Definition of Effective Training Sets for Supervised Classification of Remote Sensing Images by a Novel Cost-Sensitive Active Learning Method

This paper proposes a novel cost-sensitive active learning (CSAL) method to the definition of reliable training sets for the classification of remote sensing images with support vector machines. Unlike standard active learning (AL) methods, the proposed CSAL method redefines AL by assuming that the labeling cost of samples during ground survey is not identical, but depends on both the samples accessibility and the traveling time to the considered locations. The proposed CSAL method selects the most informative samples on the basis of three criteria: 1) uncertainty; 2) diversity; and 3) labeling cost. The labeling cost of the samples is modeled by a novel cost function that exploits ancillary data such as the road network map and the digital elevation model of the considered area. In the proposed method, the three criteria are applied in two consecutive steps. In the first step, the most uncertain samples are selected, whereas in the second step the uncertain samples that are diverse and have low labeling cost are chosen. In order to select the uncertain samples that optimize the diversity and cost criteria, we propose two different optimization algorithms. The first algorithm is defined on the basis of a sequential forward selection optimization strategy, whereas the second one relies on a genetic algorithm. Experimental results show the effectiveness of the proposed CSAL method compared to standard AL methods that neglect the labeling cost.