Query: water segmentation

title: Different Color Spaces In Deep Learning Based Water Segmentation For Autonomous Marine Operations
abstract: For autonomous unmanned surface vehicles (USV) operations, it is important to be able to observe the surroundings using visual information. Water segmentation is a task where the water surface is recognized and separated from everything else. The algorithm performing the segmentation must be robust, because safety is the most important feature of autonomous USVs. This is especially challenging in many USV applications, where the rapidly changing weather and lighting conditions can cause significant distribution shifts. In this study, we analyze the robustness of different color spaces (e.g. RGB and HSV) for water segmentation and consider how to use different color channels in training and testing to maximize the robustness. We evaluate the segmentation performance on a challenging completely unseen test dataset, recorded in vastly different conditions and with different equipment.

title: An Efficient Water Segmentation Method for SAR Images
abstract: Water segmentation is a fundamental step for the information processing of SAR image, which plays an important role in ship detection, disaster monitoring and other applications. Because of the complexity of scenario in the SAR images, water segmentation of SAR image is a challenging task. Fewer convolutional neural networks (CNNs) have been developed for SAR image water segmentation in recent years, the accuracy and speed of CNN for water segmentation can be further improved. In this paper, we established a SAR water segmentation dataset based on the GF3 satellite data. then an improved water segmentation network based on Bilateral Segmentation Network (BiSeNet) is proposed. Further we propose a loss function based on edge area and a novel training data generation method to improve the segmentation ability of the network. Experimental results based on water segmentation dataset show that the proposed segmentation method has better segmentation accuracy and speed.

title: DEEP LEARNING APPLIED TO WATER SEGMENTATION
abstract: The use of deep learning (DL) with convolutional neural networks (CNN) to monitor surface water can be a valuable supplement to costly and labour intense standard gauging stations. This paper presents the application of a recent CNN semantic segmentation method (SegNet) to automatically segment river water in imagery acquired by RGB sensors. This approach can be used as a new supporting tool because there are only a few studies using DL techniques to monitor water resources. The study area is a medium scale river (Wesenitz) located in the East of Germany. The captured images reflect different periods of the day over a period of approximately 50 days, allowing for the analysis of the river in different environmental conditions and situations. In the experiments, we evaluated the input image resolutions of 256 x 256 and 512 x 512 pixels to assess their influence on the performance of river segmentation. The performance of the CNN was measured with the pixel accuracy and IoU metrics revealing an accuracy of 98% and 97% respectively, for both resolutions, indicating that our approach is efficient to segment water in RGB imagery.

title: Deep Learning Based Water Segmentation for Autonomous Surface Vessel

title: High Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: a Novel Dataset and Evaluation
abstract: Even though Unmanned Surface Vehicles (USVs) are increasingly used to perform various laborious and expensive offshore tasks, they still require an extensive dedicated crew supporting and ensuring the safety of their operations. The recent developments in computer vision and robotics further fueled the interest on developing autonomous USVs that will overcome the aforementioned limitations, unleashing their full potential. One of the most vital and fundamental tasks in order to automate and ensure the safety of USV operations is to perform water segmentation. Despite the importance of developing such segmentation methods, there is a lack of highresolution publicly available datasets, which are suitable for training and evaluating deep learning methods. The main contribution of this paper is collecting, annotating and releasing a publicly available high resolution dataset for developing deep learning algorithms for water segmentation in a Nordic lake environment. Furthermore, we adapt a deep learning algorithm previously applied for road segmentation to the water segmentation task. While the algorithm obtains a high accuracy, the results also allow for identifying critical limitations of the approach. Finally, we propose and evaluate a novel lightweight fully convolutional neural network architecture, fully adapted to the needs of water segmentation from highresolution images.

title: A Water Segmentation Algorithm for SAR Image Based on Dense Depthwise Separable Convolution
abstract: Water segmentation of real SAR images is of great significance in military and civilian applications such as ship target detection and disaster monitoring. To solve the issues of poor robustness and inaccurate segmentation of traditional water segmentation algorithms, this paper first establishes a SAR water segmentation dataset based on the GF3 satellite and then presents a segmentation network architecture based on depthwise separable convolution. The network takes real SAR images as inputs, extracts high dimensional features through depthwise separable and dilated convolutions, constructs an up sampling and decoding module based on bilinear interpolation, and then outputs the corresponding segmentation results. The segmentation results of a water segmentation dataset show that the proposed segmentation method remarkably improves the segmentation accuracy, the segmentation robustness and running speed than traditional method. Therefore, the findings demonstrate the excellent practical engineering value of the proposed algorithm.

title: SAR image land and water segmentation algorithm based on hybrid fuzzy

title: Semi automated land/water segmentation of multi spectral imagery
abstract: Segmentation of land and water regions is necessary in many applications involving analysis of remote sensing imagery. Not only is manual segmentation of these regions prone to considerable subjective variability, but the large volume of imagery collected by modern platforms makes manual segmentation extremely tedious to perform, particularly in applications that require frequent re measurement. This paper examines a robust, semi automated approach that utilizes simple and efficient machine learning algorithms to perform supervised classification of multi spectral image data into land and water regions. By combining the four wavelength bands widely available in imaging platforms such as IKONOS, QuickBird, and GeoEye 1 with basic texture metrics, high quality segmentation can be achieved. An efficient workflow was created by constructing a Graphical User Interface (GUI) to these machine learning algorithms.

title: X SAR SpotLigh images feature selection and water segmentation
abstract: In this paper we address the feature selection problem for X SAR images and further the segmentation of specific chosen classes. After defining a suitable feature space for X SAR images we select the most significant ones via a supervised machine learning approach: the 1 norm SVM. The selected features will be used for segmentation purposes, in order to segment water areas from the background. We shall see that the most relevant features are based on texture elements. So the segmentation is texture based and achieved with variational calculus and level set methods. The work is mainly focused on urban park X SAR SpotLight images, where lakes and rivers are often present. The images are collected with the COSMO SkyMed satellites constellation, equipped with a SAR sensor.

title: Water segmentation and classification