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Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+). Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community.

The rise and decline of foreign entry strategies in transition economies is an important yet largely overlooked issue in the literature. This study is a step toward filling this gap by examining how mimetic entry within reference groups and the emergence of a competing strategy affect the bandwagon phenomenon of a dominant strategy in the context of China, where international equity joint ventures (EJVs) used to be a dominant entry strategy among foreign firms in the 1990s. Findings from a sample of 1,123 EJVs formed in China's non‐restricted industries from 1990 to 2003 show that the impact of home and host‐country industry effects are not symmetric between the EJV rise and decline periods. Cross‐border merger and acquisition (M&A) as a competing strategy has an important impact during the EJV decline period but not the rise period. The interactive effects between EJV and M&A strategies occur only in the host‐country industries. We discuss such results and offer suggestions for future research. Copyright © 2007 John Wiley & Sons, Ltd.

In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference.

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