Channel bar feature extraction for a mining-contaminated river using high-spatial multispectral remote-sensing imagery |
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| Authors: | Caixia Wang Robert T Pavlowsky Qunying Huang Charles Chang |
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| Institution: | 1. Department of Geomatics, University of Alaska Anchorage, 3211 Providence Drive, ENGR 330C, Anchorage, AK 99508, USA;2. Department of Geography, Geology and Planning, Missouri State University, 901 S. National Ave., Springfield, MO 65897, USA;3. Department of Geography, University of Wisconsin – Madison, 550 N. Park St., Madison, WI 53706, USA;4. Gaylord Nelson Institute, University of Wisconsin – Madison, Madison, WI, 53706, USA |
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| Abstract: | Mapping and monitoring changes of geomorphological features over time are important for understanding fluvial process and effects of its controlling factors. Using high spatial resolution multispectral images has become common practice in the mapping as these images become widely available. Traditional pixel-based classification relies on statistical characteristics of single pixels and performs poorly in detailed mapping using high resolution multispectral images. In this work, we developed a hybrid method that detects and maps channel bars, one of the most important geomorphological features, from high resolution multispectral aerial imagery. This study focuses on the Big River which drains the Ozarks Plateaus region in southeast Missouri and the Old Lead Belt Mining District which was one of the largest producers of lead worldwide in the early and middle 1900s. Mapping and monitoring channel bars in the Big River is essential for evaluating the fate of contaminated mining sediment released to the Big River. The dataset in this study is 1 m spatial resolution and is composed of four bands: Red (Band 3), Green (Band 2), Blue (Band 1) and Near-Infrared (Band 4). The proposed hybrid method takes into account both spectral and spatial characteristics of single pixels, those of their surrounding contextual pixels and spatial relationships of objects. We evaluated its performance by comparing it with two traditional pixel-based classifications including Maximum Likelihood (MLC) and Support Vector Machine (SVM). The findings indicate that derived characteristics from segmentation and human knowledge can highly improve the accuracy of extraction and our proposed method was successful in extracting channel bars from high spatial resolution images. |
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| Keywords: | feature extraction object-based classification images river |
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