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ISPRS Int. J. Geo-Inf. 2014, 3(4), 1491-1511; doi:10.3390/ijgi3041491

Predicting Relevant Change in High Resolution Satellite Imagery

Center for Geospatial Intelligence, University of Missouri, Columbia, MO 65211, USA
Received: 4 September 2014 / Revised: 15 November 2014 / Accepted: 12 December 2014 / Published: 22 December 2014
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Abstract

With the ever increasing volume of remote sensing imagery collected by satellite constellations and aerial platforms, the use of automated techniques for change detection has grown in importance, such that changes in features can be quickly identified. However, the amount of data collected surpasses the capacity of imagery analysts. In order to improve the effectiveness and efficiency of imagery analysts performing data maintenance activities, we propose a method to predict relevant changes in high resolution satellite imagery based on human annotations on selected regions of an image. We study a variety of classifiers in order to determine which is most accurate. Additionally, we experiment with a variety of ways in which a diverse set of training data can be constructed to improve the quality of predictions. The proposed method aids in the analysis of change detection results by using various classifiers to develop a relevant change model that can be used to predict the likelihood of other analyzed areas containing a relevant change or not. These predictions of relevant change are useful to analysts, because they speed the interrogation of automated change detection results by leveraging their observations of areas already analyzed. A comparison of four classifiers shows that the random forest technique slightly outperforms other approaches. View Full-Text
Keywords: change detection; classification; remote sensing change detection; classification; remote sensing
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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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MDPI and ACS Style

Klaric, M. Predicting Relevant Change in High Resolution Satellite Imagery. ISPRS Int. J. Geo-Inf. 2014, 3, 1491-1511.

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