Change Detection and Image Time-Series Analysis 1. Группа авторов

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СКАЧАТЬ measures for multivariate SAR data. The last four chapters focus more on applications. Chapter 4 addresses functional representations (wavelets and convolutional neural network filters) for feature extraction in an unsupervised approach. It proposes anomaly detection and functional evolution clustering from this framework by using relative entropy information extracted from SAR data decomposition. Chapter 5 deals with the selection of metrics that are sensitive to snow state variation in the context of the cryosphere, with a focus on mountain areas. Metrics such as cross-correlation ratios and Hausdorff distance are analyzed with respect to optimal reference images to identify optimal thresholding strategies for the detection of wet snow by using Sentinel-1 image time series. Chapter 6 presents time series analysis in the context of spatio-temporal forecasting and monitoring fast-moving meteorological events such as cyclones. The application benefits from the fusion of remote sensing data under the fractional dynamic field assumption on the cyclone behavior. Chapter 7 proposes an analysis based on characteristic points for texture modeling with graph theory. Such an approach overcomes issues arising from large-size dense neighborhoods that affect spatial context-based approaches. The application proposed in this chapter concerns glacier flow measurement in bitemporal images. Chapter 8 focuses on detecting new land-cover types by classification-based change detection or feature/pixel-based change detection. Monitoring the construction of new buildings in urban and suburban scenarios at a large regional scale by means of Sentinel-1 and -2 images is considered as an application. Chapter 9 focuses on the statistical modeling of classes in the difference image and derives from scratch a multiclass model for it in the context of change vector analysis.

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 and Statistical Variance–Covariance Matrices
pdf	Probability Density Function

      1

      Unsupervised Change Detection in Multitemporal Remote Sensing Images

       Sicong LIU1, Francesca BOVOLO2, Lorenzo BRUZZONE3, Qian DU4 and Xiaohua TONG1

       1Tongji University, Shanghai, China

       2Fondazione Bruno Kessler, Trento, Italy

       3University of Trento, Italy

       4Mississippi State University, Starkville, USA

1.1. Introduction

Remote sensing satellites have a great potential to recurrently monitor the dynamic changes of the Earth’s surface in a wide geographical area, and contribute substantially to our current understanding of the land-cover and land-use changes (Bruzzone and Bovolo 2013; Song et al. 2018; Liu et al. 2019c). Scientifically understanding land changes is also essential for analyzing environmental evolution and anthropic phenomena, especially when studying the global change and its impact on human society. Thanks to the satellite revisit property, both long-term (e.g. yearly) and short-term (e.g. daily) satellite observations produce a huge amount of multitemporal images in the data archive (Liu et al. 2015). Based on the analysis of multitemporal data, land-cover changes can be automatically discovered and detected, where knowledge of changes can also СКАЧАТЬ