This paper reviewed major remote sensing image classification techniques, including pixel-wise, sub-pixel-wise, and object-based image classification methods, and highlighted the importance of incorporating spatio-contextual information in remote sensing image classification. Further, this paper grouped spatio-contextual analysis techniques into three major categories, including 1) texture extraction, 2) Markov random fields (MRFs) modeling, and 3) image segmentation and object-based image analysis. Finally, this paper argued the necessity of developing geographic information analysis models for spatial-contextual classifications using two case studies.

No abstract

No abstract

Abstract-Localization is crucial for wireless ad hoc and sensor networks. As the distance-measurement ranges are often less than the communication ranges for many ranging systems, most communication-dense wireless networks are localization-sparse. Consequently, existing algorithms fail to provide accurate localization supports. In order to address this issue, by introducing the concept of component, we group nodes into components so that nodes are able to better share ranging and anchor knowledge. Operating on the granularity of components, our design, CALL, relaxes two essential restrictions in localization: the node ordering and the anchor distribution. Compared to previous designs, CALL is proven to be able to locate the same number of nodes using the least information. We evaluate the effectiveness of CALL through extensive simulations. The results show that CALL locates 90% nodes in a network with average degree 7.5 and 5% anchors, which outperforms the state-of-the-art design Sweeps by about 40%.Index Terms-Component-based, finite mergence, localization, node-based, ranging-model-based estimation (RMBE).