Devis Tuia

Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods

The technological evolution of optical sensors over the last few decades has provided remote sensing analysts with rich spatial, spectral, and temporal information. In particular, the increase in spectral resolution of hyperspectral images (HSIs) and infrared sounders opens the doors to new application domains and poses new methodological challenges in data analysis. HSIs allow the characterization of objects of interest (e.g., land-cover classes) with unprecedented accuracy, and keeps inventories up to date. Improvements in spectral resolution have called for advances in signal processing and exploitation algorithms. This article focuses on the challenging problem of hyperspectral image classification, which has recently gained in popularity and attracted the interest of other scientific disciplines such as machine learning, image processing, and computer vision. In the remote sensing community, the term classification is used to denote the process that assigns single pixels to a set of classes, while the term segmentation is used for methods aggregating pixels into objects and then assigned to a class.

Supervised change detection in VHR images using contextual information and support vector machines

In this paper we study an effective solution to deal with supervised change detection in very high geometrical resolution (VHR) images. High within-class variance as well as low between-class variance that characterize this kind of imagery make the detection and classification of ground cover transitions a difficult task. In order to achieve high detection accuracy, we propose the inclusion of spatial and contextual information issued from local textural statistics and mathematical morphology. To perform change detection, two architectures, initially developed for medium resolution images, are adapted for VHR: Direct Multi-date Classification and Difference Image Analysis. To cope with the high intra-class variability, we adopted a nonlinear classifier: the Support Vector Machines (SVM). The proposed approaches are successfully evaluated on two series of pansharpened QuickBird images.

Multi-Modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009–2010 Data Fusion Contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Active Learning: Any Value for Classification of Remotely Sensed Data?

Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative pixels, while the training set should be as compact as possible. Active learning heuristics provide capability to select unlabeled data that are the “most informative” and to obtain the respective labels, contributing to both goals. Characteristics of remotely sensed image data provide both challenges and opportunities to exploit the potential advantages of active learning. We present an overview of active learning methods, then review the latest techniques proposed to cope with the problem of interactive sampling of training pixels for classification of remotely sensed data with support vector machines (SVMs). We discuss remote sensing specific approaches dealing with multisource and spatially and time-varying data, and provide examples for high-dimensional hyperspectral imagery.

Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks

Semantic labeling (or pixel-level land-cover classification) in ultrahigh-resolution imagery (<10 cm) requires statistical models able to learn high-level concepts from spatial data, with large appearance variations. Convolutional neural networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction. In this paper, we present a CNN-based system relying on a downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them back to the original resolution by deconvolutions. By doing so, the CNN learns to densely label every pixel at the original resolution of the image. This results in many advantages, including: 1) the state-of-the-art numerical accuracy; 2) the improved geometric accuracy of predictions; and 3) high efficiency at inference time. We test the proposed system on the Vaihingen and Potsdam subdecimeter resolution data sets, involving the semantic labeling of aerial images of 9- and 5-cm resolution, respectively. These data sets are composed by many large and fully annotated tiles, allowing an unbiased evaluation of models making use of spatial information. We do so by comparing two standard CNN architectures with the proposed one: standard patch classification, prediction of local label patches by employing only convolutions, and full patch labeling by employing deconvolutions. All the systems compare favorably or outperform a state-of-the-art baseline relying on superpixels and powerful appearance descriptors. The proposed full patch labeling CNN outperforms these models by a large margin, also showing a very appealing inference time.

SVM Active Learning Approach for Image Classification Using Spatial Information

In the last few years, active learning has been gaining growing interest in the remote sensing community in optimizing the process of training sample collection for supervised image classification. Current strategies formulate the active learning problem in the spectral domain only. However, remote sensing images are intrinsically defined both in the spectral and spatial domains. In this paper, we explore this fact by proposing a new active learning approach for support vector machine classification. In particular, we suggest combining spectral and spatial information directly in the iterative process of sample selection. For this purpose, three criteria are proposed to favor the selection of samples distant from the samples already composing the current training set. In the first strategy, the Euclidean distances in the spatial domain from the training samples are explicitly computed, whereas the second one is based on the Parzen window method in the spatial domain. Finally, the last criterion involves the concept of spatial entropy. Experiments on two very high resolution images show the effectiveness of regularization in spatial domain for active learning purposes.

Semisupervised Classification of Remote Sensing Images With Active Queries

We propose a semiautomatic procedure to generate land cover maps from remote sensing images. The proposed algorithm starts by building a hierarchical clustering tree, and exploits the most coherent pixels with respect to the available class information. For a given amount of labeled pixels, the algorithm returns both classification and confidence maps. Since the quality of the map depends of the number and informativeness of the labeled pixels, active learning methods are used to select the most informative samples to increase confidence in class membership. Experiments on four different data sets, accounting for hyperspectral and multispectral images at different spatial resolutions, confirm the effectiveness of the proposed approach, and how active learning techniques reduce the uncertainty of the classification maps. Specifically, more accurate results with fewer labeled samples are obtained. Inclusion of spatial information in the classifiers drastically improves the classification accuracy, leading to faster convergence curves and tighter confidence intervals. In conclusion, the presented algorithm provides efficient image classification and, at the same time, yields a confidence map that may be very useful in many Earth observation applications.

Unsupervised Change Detection With Kernels

In this letter, an unsupervised kernel-based approach to change detection is introduced. Nonlinear clustering is utilized to partition in two a selected subset of pixels representing both changed and unchanged areas. Once the optimal clustering is obtained, the learned representatives of each group are exploited to assign all the pixels composing the multitemporal scenes to the two classes of interest. Two approaches based on different assumptions of the difference image are proposed. The first accounts for the difference image in the original space, while the second defines a mapping describing the difference image directly in feature spaces. To optimize the parameters of the kernels, a novel unsupervised cost function is proposed. An evidence of the correctness, stability, and superiority of the proposed solution is provided through the analysis of two challenging change-detection problems.

Graph Matching for Adaptation in Remote Sensing

We present an adaptation algorithm focused on the description of the data changes under different acquisition conditions. When considering a source and a destination domain, the adaptation is carried out by transforming one data set to the other using an appropriate nonlinear deformation. The eventually nonlinear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the samples in one domain are projected onto the other, thus allowing the application of any classifier or regressor in the transformed domain. Experiments on challenging remote sensing scenarios, such as multitemporal very high resolution image classification and angular effects compensation, show the validity of the proposed method to match-related domains and enhance the application of cross-domains image processing techniques.

Semisupervised Manifold Alignment of Multimodal Remote Sensing Images

We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images. The problem is recurrent when a set of multitemporal, multisource, multisensor, and multiangular images is available. In these situations, images should ideally be spatially coregistered, corrected, and compensated for differences in the image domains. Such procedures require massive interaction of the user, involve tuning of many parameters and heuristics, and are usually applied separately. Changes of sensors and acquisition conditions translate into shifts, twists, warps, and foldings of the (typically nonlinear) manifolds where images lie. The proposed semisupervised manifold alignment (SS-MA) method aligns the images working directly on their manifolds and is thus not restricted to images of the same resolutions, either spectral or spatial. SS-MA pulls close together samples of the same class while pushing those of different classes apart. At the same time, it preserves the geometry of each manifold along the transformation. The method builds a linear invertible transformation to a latent space where all images are alike and reduces to solving a generalized eigenproblem of moderate size. We study the performance of SS-MA in toy examples and in real multiangular, multitemporal, and multisource image classification problems. The method performs well for strong deformations and leads to accurate classification for all domains. A MATLAB implementation of the proposed method is provided at http://isp. uv.es/code/ssma.htm.