Surface Water Mapping by Deep Learning

Isikdogan, Furkan; Bovik, Alan C.; Passalacqua, Paola

doi:10.1109/jstars.2017.2735443

Cited by 234 publications

(156 citation statements)

References 30 publications

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“…In this model, each neuron in each layer need to be fully connected. Compared with traditional artificial neural network, the more layer is set in multi-layer perceptron to deeply learn the feature of input data (Isikdogan et al, 2017).…”

Section: Water Extraction Algorithmmentioning

confidence: 99%

“…Recent progress in deep learning has shown the promising solution for the target detection and image classification across the various image processing fields (Guo et al, 2016). Despite the convolutional neural networks under the deep learning architecture has been widely used to identify the target in remote sensing images by using labelled training sample (Isikdogan et al, 2017), few studies have reported the application of deep learning to remote sensing data at large scale. Therefore, this study aims to explore the potential of the multi-layer perceptron based on deep learning framework for identifying water body in Landsat OLI images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Water Bodies in Landsat8 Oli Image Using Deep Learning

Jiang

Long

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Water Extraction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Water Bodies in Landsat8 Oli Image Using Deep Learning

Jiang

Long

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…However, their generalization ability at global scale and across images from different sensors has proven difficult. Recently, studies that use Convolutional Neural Networks (CNNs), which learn features directly from raw images by combining convolutional and pooling layers, reported superior accuracy and generalization ability compared to rule-based and classical machine learning approaches with hand-crafted features (Chen et al, 2018;Isikdogan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Water Bodies in Multi-Spectral Satellite Images for Situational Awareness in Emergency Response

Wieland

Martinis

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Satellite-based crisis information is frequently requested in the context of flood disasters to gain rapidly situational awareness and to prioritize response actions under often limited resources during emergency response. To assure that information products have the highest possible spatial, temporal and thematic resolutions, it is critical to be able to simultaneously analyse data from a large variety of satellite sensors. In this contribution, we present a solution to rapidly extract water bodies from Landsat TM, ETM+, OLI and Sentinel-2 for up-to-date situational awareness during emergency response. A convolutional neural network is used to segment water extent in these images, while clouds, cloud shadows and snow / ice are specifically handled by the network to remove potential bias from any downstream analysis. Atmospheric correction, post-processing and ancillary data are not required. To distinguish flood from permanent water we present a reference water mask that is derived by means of time-series analysis of archive imagery. Compared to widely-used mono-temporal reference water masks, it can be adapted to any area and time of interest. This study builds up on previous work of the authors and presents new results from recent flood disasters in Germany, Peru, China, India and Mozambique, as well as a flood monitoring application centred on the Indian state of Kerala. The processing chain produces very high overall accuracy and Kappa coefficient (>0.87) and shows consistent performance throughout a monitoring period of 12 months that covers 143 Landsat OLI and Sentinel-2 images.

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“…Although there are a number of previous works [5], [6] that performed flooding detection using remote sensing data in conjunction with elevation maps, augmenting the amount and variety of information available for an effective prediction, our work is one of the first specifically focused on identifying flooding areas on only high resolution imagery using deep learning-based approaches. Furthermore, some works [7], [8] performing surface water segmentation may be suitable for flooding detection, given the high similarity between these tasks. However, the focus of this paper is exclusively on flooding area identification from satellite images took during and shortly after a flood event.…”

Section: Introductionmentioning

confidence: 99%

Exploiting ConvNet Diversity for Flooding Identification

Nogueira

Fadel

Dourado

et al. 2018

IEEE Geosci. Remote Sensing Lett.

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Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure towards flood monitoring is based on identifying the area most vulnerable to flooding, which gives authorities relevant regions to focus. In this work, we propose several methods to perform flooding identification in highresolution remote sensing images using deep learning. Specifically, some proposed techniques are based upon unique networks, such as dilated and deconvolutional ones, while other was conceived to exploit diversity of distinct networks in order to extract the maximum performance of each classifier. Evaluation of the proposed methods were conducted in a high-resolution remote sensing dataset. Results show that the proposed algorithms outperformed state-of-the-art baselines, providing improvements ranging from 1 to 4% in terms of the Jaccard Index.

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Surface Water Mapping by Deep Learning

Cited by 234 publications

References 30 publications

Detecting Water Bodies in Landsat8 Oli Image Using Deep Learning

Detecting Water Bodies in Landsat8 Oli Image Using Deep Learning

Semantic Segmentation of Water Bodies in Multi-Spectral Satellite Images for Situational Awareness in Emergency Response

Exploiting ConvNet Diversity for Flooding Identification

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