A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques |
| |
| Institution: | 1. The State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;2. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;1. Airbus Defence and Space, 6 Dominus Way, Meridian Business Park, Leicester LE19 1RP, UK;2. Department of Geography, University of Leicester, Leicester LE1 7RH, UK |
| |
| Abstract: | Normally, to detect surface water changes, water features are extracted individually using multi-temporal satellite data, and then analyzed and compared to detect their changes. This study introduced a new approach for surface water change detection, which is based on integration of pixel level image fusion and image classification techniques. The proposed approach has the advantages of producing a pansharpened multispectral image, simultaneously highlighting the changed areas, as well as providing a high accuracy result. In doing so, various fusion techniques including Modified IHS, High Pass Filter, Gram Schmidt, and Wavelet-PC were investigated to merge the multi-temporal Landsat ETM+ 2000 and TM 2010 images to highlight the changes. The suitability of the resulting fused images for change detection was evaluated using edge detection, visual interpretation, and quantitative analysis methods. Subsequently, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML) classification techniques were applied to extract and map the highlighted changes. Furthermore, the applicability of the proposed approach for surface water change detection was evaluated in comparison with some common change detection methods including image differencing, principal components analysis, and post classification comparison. The results indicate that Lake Urmia lost about one third of its surface area in the period 2000–2010. The results illustrate the effectiveness of the proposed approach, especially Gram Schmidt-ANN and Gram Schmidt-SVM for surface water change detection. |
| |
| Keywords: | Surface water Change detection Image fusion Classification |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
