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Remote Sens. 2015, 7(11), 15014-15045; doi:10.3390/rs71115014

Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge

1
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
3
Department of Statistics, University of California, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Soe Myint, Xiaofeng Li and Prasad S. Thenkabail
Received: 18 August 2015 / Revised: 12 October 2015 / Accepted: 3 November 2015 / Published: 10 November 2015
View Full-Text   |   Download PDF [2978 KB, uploaded 10 November 2015]   |  

Abstract

It is a challenging problem to efficiently interpret the large volumes of remotely sensed image data being collected in the current age of remote sensing “big data”. Although human visual interpretation can yield accurate annotation of remote sensing images, it demands considerable expert knowledge and is always time-consuming, which strongly hinders its efficiency. Alternatively, intelligent approaches (e.g., supervised classification and unsupervised clustering) can speed up the annotation process through the application of advanced image analysis and data mining technologies. However, high-quality expert-annotated samples are still a prerequisite for intelligent approaches to achieve accurate results. Thus, how to efficiently annotate remote sensing images with little expert knowledge is an important and inevitable problem. To address this issue, this paper introduces a novel active clustering method for the annotation of high-resolution remote sensing images. More precisely, given a set of remote sensing images, we first build a graph based on these images and then gradually optimize the structure of the graph using a cut-collect process, which relies on a graph-based spectral clustering algorithm and pairwise constraints that are incrementally added via active learning. The pairwise constraints are simply similarity/dissimilarity relationships between the most uncertain pairwise nodes on the graph, which can be easily determined by non-expert human oracles. Furthermore, we also propose a strategy to adaptively update the number of classes in the clustering algorithm. In contrast with existing methods, our approach can achieve high accuracy in the task of remote sensing image annotation with relatively little expert knowledge, thereby greatly lightening the workload burden and reducing the requirements regarding expert knowledge. Experiments on several datasets of remote sensing images show that our algorithm achieves state-of-the-art performance in the annotation of remote sensing images and demonstrates high potential in many practical remote sensing applications. View Full-Text
Keywords: information mining; remote sensing image annotation; image clustering; active clustering; expert knowledge information mining; remote sensing image annotation; image clustering; active clustering; expert knowledge
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xia, G.-S.; Wang, Z.; Xiong, C.; Zhang, L. Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge. Remote Sens. 2015, 7, 15014-15045.

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