Object-based change detection
基于对象的变化检测
Characterizations of land-cover dynamics are among the most important applications of Earth observation data, providing insights into management, policy and science. Recent progress in remote sensing and associated digital image processing offers unprecedented opportunities to detect changes in land cover more accurately over increasingly large areas, with diminishing costs and processing time. The advent of high-spatial-resolution remote-sensing imagery further provides opportunities to apply change detection with object-based image analysis (OBIA), that is, object-based change detection (OBCD). When compared with the traditional pixel-based change paradigm, OBCD has the ability to improve the identification of changes for the geographic entities found over a given landscape. In this article, we present an overview of the main issues in change detection, followed by the motivations for using OBCD as compared to pixel-based approaches. We also discuss the challenges caused by the use of objects in change detection and provide a conceptual overview of solutions, which are followed by a detailed review of current OBCD algorithms. In particular, OBCD offers unique approaches and methods for exploiting high-spatial-resolution imagery, to capture meaningful detailed change information in a systematic and repeatable manner, corresponding to a wide range of information needs.
土地覆盖动态特征刻画是地球观测数据最重要的应用之一，它为管理、政策和科学提供了深刻的见解。而最近，在遥感和相关数字图像处理方面取得的进展则为我们提供了前所未有的机会，可以在越来越大的地区更准确地检测土地覆盖的变化，从而减少成本和处理时间。高空间分辨率遥感图像的出现更进一步提供了将变化检测应用于基于对象的图像分析(简称OBIA)，即基于对象的变化检测(简称OBCD)的机会。与传统的基于像素的变化范式相比，OBCD能够更好地识别并发现在给定景观上地理实体的变化。在本文中，我们将概述变化检测中的主要问题，紧接着提出与基于像素的方法相比，使用OBCD的动机。我们还讨论了在变化检测中使用对象所带来的挑战，并提供了解决方案的概念概述，随后详细回顾了现有的OBCD算法。尤其是OBCD提供了独特的途径和方法来利用高空间分辨率图像，用系统并且可重复的方式捕捉有意义的详细的变化信息，以满足广泛的信息需求。
1.Introduction
Since the advent of satellite-based Earth observation, land-cover change detection has been a major driver of developments in the analysis of remotely sensed data (Anuta and Bauer 1973, Anderson 1977, Nelson 1983, Singh 1989, Aplin 2004, Coppin et al. 2004, Lu et al. 2004). More recently, high-spatial-resolution imagery has been available from commercial operators, providing unique opportunities for detailed characterization and monitoring of forest ecosystems (Wulder et al. 2004, 2008c, Hay et al. 2005, Falkowski et al. 2009) and urban areas (Herold et al. 2002, Hay et al. 2010) and additional applications developed to address the increasingly detailed information needs (Castilla et al. 2008, Chen et al. 2011). Land-cover change refers to variations in the state or type of physical materials on the Earthrsquo;s surface, such as forests, grass, water,etc., which can be directly observed using remote-sensing techniques (Fisher et al. 2005). As human-induced changes occur at an increasingly rapid pace, and as Earth observation data become ubiquitous, remote-sensing-based monitoring systems are expected to play further crucial roles in environmental policy and decision-making.
1.介绍
自从基于卫星的地球观测出现以来，土地覆盖变化的探测一直是遥感数据分析发展的主要推动力。最近，商业经营者提供了高空间分辨率的图像，为详细描述和监测森林生态系统和城市地区提供了独特的机会，并为满足日益详细的信息需求开发了更多的应用。土地覆盖变化是指地球表面物理物质的状态或类型的变化，如森林、草、水等，可以通过遥感技术直接观察这些变化。随着人类引起的土地覆盖变化以越来越快的速度发生，并伴随着地球观测数据的普及，基于遥感的监测系统有望在环境政策和决策中发挥更重要的作用。
Accurate monitoring of land cover is a matter of utmost importance in many different fields. Satelliteor airborne-based monitoring of the Earthrsquo;s surface provides information on the interactions between anthropogenic and environmental phenomena, providing the foundation to use natural resources better (Lu et al. 2004). It enables refined policy development and the capacity to address otherwise inaccessible science questions (Cohen and Goward 2004). Remote-sensing change detection, defined by Singh (1989) as lsquo;the process of identifying differences in the state of an object or phenomenon by observing it at different timesrsquo;, provides a means to study and understand the patterns and processes of ecosystems at a range of geographical and temporal scales. While the knowledge of land-cover conditions at a given point in time is important, the dynamics or trends related to specific change conditions offer unique and often important insights, ranging from natural disaster management to atmospheric pollution dispersion. Indeed, remotely sensed imagery is an important source of data available to characterize change systematically and consistently in terrestrial ecosystems over time (Coops et al. 2006).
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