Detecting Water Bodies in Landsat8 Oli Image Using Deep Learning

Abstract

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.

Assessment of Riverbank Erosion Hotspots along the Mekong River in Cambodia Using Remote Sensing and Hazard Exposure Mapping

“…Moreover, they can effectively detect water bodies at low computational costs. In recent years, many advanced techniques have been proposed for detecting water bodies using machine learning/deep learning [42][43][44][45][46][47][48]. These techniques were developed to improve the accuracy of water body detection, especially small water bodies in complex terrains, and to overcome the limitations of spectral resolution in high-resolution images (e.g., Ikonos and Quickbird).…”

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

“…The management of this crop is changing from traditional low-density rainfed olive groves to medium-or high-density groves, mostly associated with irrigation, that are promoting the substitution of extensive crops such as wheat, barley, sunflowers, or cotton in some regions. This strong modification of olive growing systems can be clearly observed in Andalusia, one of the main olive-growing regions in the world with 46.7% of the olive-growing area in Spain [5]. From 2015 to 2019, high-and super high-density olive groves (more than 400 olive trees ha −1 ) steadily increased in area by 48.5%, from 54,140 ha in 2015 to 80,386 ha in 2019 [5,6].…”

“…With the continuous increase of RS data (images and derived information), traditional classification methods based on spectral distance-angles or probabilities are not the most appropriate since they do not take advantage of all the information efficiently [43]. The rapid development of new technologies such as machine learning (ML) or deep learning (DL) techniques in the field of RS are showing more accurate classifications and target detections [44][45][46]. To facilitate the automation of classification processes, deep learning (DL) can be a good approach.…”

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

“…Ref. [ 10 ] compared MLP, NDWI, and a maximum likelihood model for water body classification and showed that MLP performed the best. However, the maximum likelihood model could not recognize small bodies of water and thin rivers, whereas NDWI was not able to distinguish seawater from land.…”

“…The changing spatial and temporal patterns of surface water are important, in both practical and scientific terms, for water resources management, biodiversity, emergency response, and climate change [ 9 ]. More specifically, automated monitoring of water bodies is critical for adapting to climate change, water resources, ecosystem services, and the hydrological cycle, as well as for urban hydrology, which can facilitate timely flood protection planning and water quality control for public safety and health [ 10 , 11 , 12 ]. Accurate water quality monitoring is essential for developing sustainable water resource management strategies and ensuring the health of communities, ecosystems, and economies [ 13 ].…”