scholarly journals Dynamic Water Surface Detection Algorithm Applied on PROBA-V Multispectral Data

2016 ◽  
Vol 8 (12) ◽  
pp. 1010 ◽  
Author(s):  
Luc Bertels ◽  
Bruno Smets ◽  
Davy Wolfs
Keyword(s):  
Water Surface ◽  
Detection Algorithm ◽  
Multispectral Data ◽  
Surface Detection

scholarly journals A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface

2021 ◽  
Vol 2083 (3) ◽  
pp. 032090
Author(s):  
Changli Mai ◽  
Bijian Jian ◽  
Yongfa Ling
Keyword(s):  
Water Surface ◽  
Center Of Mass ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Random Fluctuation ◽  
False Detection ◽  
Detection Algorithms ◽  
Regional Center ◽  
Underwater Environment ◽  
Active Imaging

Abstract Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.

scholarly journals O limite marítimo do estuário do Sado a partir de imagens satelitais

Finisterra ◽  
2012 ◽  
Vol 30 (59/60) ◽  
Author(s):  
Eugénia Albergaria Moreira
Keyword(s):  
Bottom Sediments ◽  
Water Surface ◽  
Sea Water ◽  
Data Classification ◽  
Landsat Data ◽  
Delta Front ◽  
Water Turbidity ◽  
Estuary Mouth ◽  
Multispectral Data ◽  
Sado Estuary

SEA-SIDE LIMIT OF SADO ESTUARY IN LANDSAT IMAGERIES - The outside limit of Sado estuary corresponds to the submarine delta front, with an arcuate and digitate contour. It was individualised on the MSS Landsat data imageries. Multispectral data classification allowed to verify that the turbidity-plum of Sado estuary mouth rather corresponds to the low-sea-water surface associated to the brightness of the bottom sediments, that to the water turbidity content.

A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data

2014 ◽  
Vol 140 ◽  
pp. 704-716 ◽  
Author(s):  
J.-F. Pekel ◽  
C. Vancutsem ◽  
L. Bastin ◽  
M. Clerici ◽  
E. Vanbogaert ◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Water Surface ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data ◽  
Surface Detection

scholarly journals Water surface object detection using panoramic vision based on improved single-shot multibox detector

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Aofeng Li ◽  
Xufang Zhu ◽  
Shuo He ◽  
Jiawei Xia
Keyword(s):  
Object Detection ◽  
Water Surface ◽  
Detection Algorithm ◽  
Single Shot ◽  
Panoramic Vision ◽  
Feature Pyramid ◽  
The Mean ◽  
Detection Effect ◽  
Surface Object ◽  
Improved Algorithm

AbstractIn view of the deficiencies in traditional visual water surface object detection, such as the existence of non-detection zones, failure to acquire global information, and deficiencies in a single-shot multibox detector (SSD) object detection algorithm such as remote detection and low detection precision of small objects, this study proposes a water surface object detection algorithm from panoramic vision based on an improved SSD. We reconstruct the backbone network for the SSD algorithm, replace VVG16 with a ResNet-50 network, and add five layers of feature extraction. More abundant semantic information of the shallow feature graph is obtained through a feature pyramid network structure with deconvolution. An experiment is conducted by building a water surface object dataset. Results showed the mean Average Precision (mAP) of the improved algorithm are increased by 4.03%, compared with the existing SSD detecting Algorithm. Improved algorithm can effectively improve the overall detection precision of water surface objects and enhance the detection effect of remote objects.

scholarly journals Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment

2017 ◽  
Vol 21 (1) ◽  
pp. 43-63 ◽  
Author(s):  
Mikkel Skovgaard Andersen ◽  
Áron Gergely ◽  
Zyad Al-Hamdani ◽  
Frank Steinbacher ◽  
Laurids Rolighed Larsen ◽  
...  
Keyword(s):  
Transition Zone ◽  
Water Surface ◽  
High Energy ◽  
Green Laser ◽  
Morphological Features ◽  
Tidal Range ◽  
Surface Model ◽  
Lidar Data ◽  
Surface Detection

Abstract. The transition zone between land and water is difficult to map with conventional geophysical systems due to shallow water depth and often challenging environmental conditions. The emerging technology of airborne topobathymetric light detection and ranging (lidar) is capable of providing both topographic and bathymetric elevation information, using only a single green laser, resulting in a seamless coverage of the land–water transition zone. However, there is no transparent and reproducible method for processing green topobathymetric lidar data into a digital elevation model (DEM). The general processing steps involve data filtering, water surface detection and refraction correction. Specifically, the procedure of water surface detection and modelling, solely using green laser lidar data, has not previously been described in detail for tidal environments. The aim of this study was to fill this gap of knowledge by developing a step-by-step procedure for making a digital water surface model (DWSM) using the green laser lidar data. The detailed description of the processing procedure augments its reliability, makes it user-friendly and repeatable. A DEM was obtained from the processed topobathymetric lidar data collected in spring 2014 from the Knudedyb tidal inlet system in the Danish Wadden Sea. The vertical accuracy of the lidar data is determined to ±8 cm at a 95 % confidence level, and the horizontal accuracy is determined as the mean error to ±10 cm. The lidar technique is found capable of detecting features with a size of less than 1 m2. The derived high-resolution DEM was applied for detection and classification of geomorphometric and morphological features within the natural environment of the study area. Initially, the bathymetric position index (BPI) and the slope of the DEM were used to make a continuous classification of the geomorphometry. Subsequently, stage (or elevation in relation to tidal range) and a combination of statistical neighbourhood analyses (moving average and standard deviation) with varying window sizes, combined with the DEM slope, were used to classify the study area into six specific types of morphological features (i.e. subtidal channel, intertidal flat, intertidal creek, linear bar, swash bar and beach dune). The developed classification method is adapted and applied to a specific case, but it can also be implemented in other cases and environments.

Time reversal focusing through a human skull optimized by skull surface detection algorithm

2002 ◽  
Vol 112 (5) ◽  
pp. 2433-2433
Author(s):  
Jean‐Francois Aubry ◽  
Didier Cassereau ◽  
Mathias Fink
Keyword(s):  
Time Reversal ◽  
Detection Algorithm ◽  
Human Skull ◽  
Surface Detection
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