Foreword to the Special Issue on Machine Learning for Remote Sensing Data Processing

Tuia, Devis; Merényi, Erzsébet; Jia, Xiuping; Grana-Romay, Manuel

doi:10.1109/jstars.2014.2311915

Cited by 16 publications

(7 citation statements)

References 57 publications

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“…In this context, machine learning has proven to be the right choice to facilitate the transition from raw data to useful information. 26,27 In the remote sensing community, machine learning algorithms have been used in parallel to image processing and computer vision approaches, and remote sensingspecific constraints have been successfully integrated into the standard machine learning paradigms, such as the following:…”

Section: Machine Learning In Remote Sensingmentioning

confidence: 99%

Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response

et al. 2016

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Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today. Both the United States Federal Emergency Management Agency (FEMA) and the European Commission's Joint Research Center ( JRC) have noted that aerial imagery will inevitably present a big data challenge. The purpose of this article is to get ahead of this future challenge by proposing a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Crowdsourcing can be used to annotate features of interest in aerial images (such as damaged shelters and roads blocked by debris). These human-annotated features can then be used to train a supervised machine learning system to learn to recognize such features in new unseen images. In this article, we describe how this hybrid solution for image analysis can be implemented as a module (i.e., Aerial Clicker) to extend an existing platform called Artificial Intelligence for Disaster Response (AIDR), which has already been deployed to classify microblog messages during disasters using its Text Clicker module and in response to Cyclone Pam, a category 5 cyclone that devastated Vanuatu in March 2015. The hybrid solution we present can be applied to both aerial and satellite imagery and has applications beyond disaster response such as wildlife protection, human rights, and archeological exploration. As a proof of concept, we recently piloted this solution using very high-resolution aerial photographs of a wildlife reserve in Namibia to support rangers with their wildlife conservation efforts (SAVMAP project, http://lasig.epfl.ch/savmap). The results suggest that the platform we have developed to combine crowdsourcing and machine learning to make sense of large volumes of aerial images can be used for disaster response.

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Section: Machine Learning In Remote Sensingmentioning

confidence: 99%

Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response

et al. 2016

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“…Recently, a new trend has been oriented towards the composition of different kernels for improved learning, inspired by multiple kernel learning (MKL) approaches [19]- [22]. Some of these aspects were particularly discussed in [23], in which a detailed overview of machine learning in remote sensing data processing is given. For instance, a simple strategy to incorporate the spatial context into kernel-based classifiers is to define a pixel entity both in the spectral domain (using its spectral content) and in the spatial domain, e.g., by applying some feature extraction to its surrounding area which yields spatial (contextual) features, such as those derived using morphological analysis.…”

Section: Introductionmentioning

confidence: 99%

Multiple Feature Learning for Hyperspectral Image Classification

Li¹,

Huang²,

Gamba³

et al. 2015

IEEE Trans. Geosci. Remote Sensing

299

103

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Hyperspectral image classification has been an active topic of research in recent years. In the past, many different types of features have been extracted (using both linear and nonlinear strategies) for classification problems. On the one hand, some approaches have exploited the original spectral information or other features linearly derived from such information in order to have classes which are linearly separable. On the other hand, other techniques have exploited features obtained through nonlinear transformations intended to reduce data dimensionality, to better model the inherent nonlinearity of the original data (e.g., kernels) or to adequately exploit the spatial information contained in the scene (e.g., using morphological analysis). Special attention has been given to techniques able to exploit a single kind of features, such as composite kernel learning or multiple kernel learning, developed in order to deal with multiple kernels. However, few approaches have been designed to integrate multiple types of features extracted from both linear and nonlinear transformations. In this paper, we develop a new framework for the classification of hyperspectral scenes that pursues the combination of multiple features. The ultimate goal of the proposed framework is to be able to cope with linear and nonlinear class boundaries present in the data, thus following the two main mixing models considered for hyperspectral data interpretation. An important characteristic of the presented approach is that it does not require any regularization parameters to control the weights of considered features so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a variety of input features and hyperspectral scenes, indicate that the proposed framework for multiple feature learning provides state-of-the-art classification results without significantly increasing computational complexity.

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“…The remote sensing community has shown a great deal of interest in machine learning recently. Many journals have published special issues on machine learning for remote sensing [296,66,8,40], numerous articles have been published on the topic of rise of machine learning in remotes sensing [39,172], and all of the winning methods of the recent annual remote sensing GRSS data fusion competition [82,194,224,297] and the top performing methods on ISPRS benchmark tests [1] have been based on machine learning.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based hyperspectral image analysis: A survey

Gewali,

Monteiro,

Saber

2018

Preprint

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Hyperspectral sensors enable the study of the chemical and physical properties of scene materials remotely for the purpose of identification, detection, chemical composition analysis, and physical parameter estimation of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense. Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis. Therefore, a solid understanding of machine learning techniques have become essential for remote sensing researchers and practitioners. This paper surveys and compares recent machine learning-based hyperspectral image analysis methods published in literature. We organize the methods by the image analysis task and by the type of machine learning algorithm, and present a two-way mapping between the image analysis tasks and the types of machine learning algorithms that can be applied to them. The paper is comprehensive in coverage of both hyperspectral image analysis tasks and machine learning algorithms. The image analysis tasks considered are land cover classification, target/anomaly detection, unmixing, and physical/chemical parameter estimation. The machine learning algorithms covered are Gaussian models, linear regression, logistic regression, support vector machines, Gaussian mixture model, latent linear models, sparse linear models, Gaussian mixture models, ensemble learning, directed graphical models, undirected graphical models, clustering, Gaussian processes, Dirichlet processes, and deep learning. We also discuss the open challenges in the field of hyperspectral image analysis and explore possible future directions.

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Foreword to the Special Issue on Machine Learning for Remote Sensing Data Processing

Cited by 16 publications

References 57 publications

Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response

Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response

Multiple Feature Learning for Hyperspectral Image Classification

Machine learning based hyperspectral image analysis: A survey

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