DETECTING WATER BODIES IN LANDSAT8 OLI IMAGE USING DEEP LEARNING

作者：Wen Jiang, Guojin He, Tengfei Long, Yuan Ni · 发表于：˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 年份：2018 · DOI：10.5194/isprs-archives-xlii-3-669-2018 · 被引用次数：10 · 研究领域：Flood Risk Assessment and Management、Hydrological Forecasting Using AI、Automated Road and Building Extraction

Abstract. Water body identifying is critical to climate change, water resources, ecosystem service and hydrological cycle. Multi-layer perceptron(MLP) is the popular and classic method under deep learning framework to detect target and classify image. Therefore, this study adopts this method to identify the water body of Landsat8. To compare the performance of classification, the maximum likelihood and water index are employed for each study area. The classification results are evaluated from accuracy indices and local comparison. Evaluation result shows that multi-layer perceptron(MLP) can achieve better performance than the other two methods. Moreover, the thin water also can be clearly identified by the multi-layer perceptron. The proposed method has the application potential in mapping global scale surface water with multi-source medium-high resolution satellite data.