Change Detection and Image Time Series Analysis 2. Группа авторов

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Название Change Detection and Image Time Series Analysis 2
Автор произведения Группа авторов
Жанр Программы
Серия
Издательство Программы
Год выпуска 0
isbn 9781119882282



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sensors acquire images that measure several features at various spatial and temporal resolutions.

      RSI has become a broad multidisciplinary domain attracting scientists across the diverse fields of science and engineering. The aim of the books proposed in this RSI series is to present the state-of-the-art and available scientific knowledge about the primary sources of images acquired by optical and radar sensors. The books cover the processing methods developed by the signal and image processing community to extract useful information for end-users for an extensive range of EO applications in natural resources.

       Volume 1: Unsupervised methods

       Volume 2: Supervised methods

      The second volume of this book is dedicated to supervised methods. Chapter 1 of this volume addresses the fusion of multisensor, multiresolution and multitemporal data. This chapter reviews recent advances in the literature and proposes two supervised Markov random field-based solutions: one relies on a quadtree and the second one is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel-based methods for time series classification from the earliest shallow-learning methods to the most recent deep learning-based approaches. This chapter also includes best practices for reference data preparation and management, which are crucial tasks in supervised methods. Chapter 3 focuses on very high spatial resolution data time series and the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 focuses on the challenges of dense time series analysis, including pre-processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations, Chapters 5 and 6 propose extensive evaluations of the methodologies used to produce earthquake-induced change maps, with an emphasis on their strengths and shortcomings (Chapter 5) and the deep learning systems in the context of multiclass multilabel change-of-state classification on glacier observations (Chapter 6).

      This book covers both methodological and application topics. From the methodological viewpoint, contributions are provided with respect to feature extraction and a large number of evaluation metrics for change detection, classification and forecasting issues. Analysis has been performed in both bitemporal images and time series, illustrating both unsupervised and supervised methods and considering both binary- and multiclass outputs. Several applications are mentioned in the chapters, including agriculture, urban areas and cryosphere analysis, among others. This book provides a deep insight into the evolution of change detection and time series analysis in the state-of-the-art, as well as an overview of the most recent developments.

      July 2021

      List of Notations

Image Time Series: time index k and pixel position (p, q)
Vector Image Time Series: band/spectral index c
Matrix Image Time Series: (polarimetric indices (u, v))
Sets of Natural Numbers, Integers, Real and Complex Numbers
μ, μ Means of Random Variables and Random Vectors
C, Σ Physical