Atlantic herring low frequency target strength estimation from ocean acoustics wageguide remote sensing (OAWRS) data in the Gulf of Maine over 10 days of observation.

2009 ◽  
Vol 125 (4) ◽  
pp. 2549-2549
Author(s):  
Duong Duy Tran ◽  
Zheng Gong ◽  
Nikhila Srikanth ◽  
Mark Andrews ◽  
Purnima Ratilal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Gulf Of Maine ◽  
Low Frequency ◽  
Target Strength ◽  
Atlantic Herring ◽  
Ocean Acoustics

Spawning Behaviour and Spatial Distribution of Atlantic Herring on Georges Bank Revealed by Ocean Acoustics Waveguide Remote Sensing

2008 ◽  
Vol 123 (5) ◽  
pp. 3103-3103 ◽  
Author(s):  
Purnima Ratilal ◽  
Zheng Gong ◽  
Daniel Cocuzzo ◽  
Mark Andrews ◽  
Srinivasan Jagannathan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Atlantic Herring ◽  
Georges Bank ◽  
Ocean Acoustics ◽  
Spawning Behaviour

Spawning behavior and spatial distribution of Atlantic herring on Georges Bank revealed by ocean acoustics waveguide remote sensing

2007 ◽  
Vol 122 (5) ◽  
pp. 3003
Author(s):  
Zheng Gong ◽  
Daniel Cocuzzo ◽  
Mark Andrews ◽  
Purnima Ratilal ◽  
Srinivasan Jagannathan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Atlantic Herring ◽  
Georges Bank ◽  
Ocean Acoustics ◽  
Spawning Behavior

A Low-Frequency Parametric Research Tool for Ocean Acoustics

1980 ◽  
pp. 467-483 ◽  
Author(s):  
T. G. Muir ◽  
L. A. Thompson ◽  
L. R. Cox ◽  
H. G. Frey
Keyword(s):  
Low Frequency ◽  
Research Tool ◽  
Ocean Acoustics

Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images

2014 ◽  
Vol 12 (05) ◽  
pp. 1461010 ◽  
Author(s):  
Changmiao Hu ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Near Infrared ◽  
Low Frequency ◽  
Median Filtering ◽  
Remote Sensing Images ◽  
Infrared Band ◽  
Remote Sensing Image Processing

In recent years, China's demand for satellite remote sensing images increased. Thus, the country launched a series of satellites equipped with high-resolution sensors. The resolutions of these satellites range from 30 m to a few meters, and the spectral range covers the visible to the near-infrared band. These satellite images are mainly used for environmental monitoring, mapping, land surface classification and other fields. However, haze is an important factor that often affects image quality. Thus, dehazing technology is becoming a critical step in high-resolution remote sensing image processing. This paper presents a rapid algorithm for dehazing based on a semi-physical haze model. Large-scale median filtering technique is used to extract large areas of bright, low-frequency information from images to estimate the distribution and thickness of the haze. Four images from different satellites are used for experiment. Results show that the algorithm is valid, fast, and suitable for the rapid dehazing of numerous large-sized high-resolution remote sensing images in engineering applications.

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

2016 ◽  
Vol 39 (2) ◽  
pp. 183-193 ◽  
Author(s):  
Lu Liu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Inverse Transformation ◽  
Unsharp Masking ◽  
Mean Filter ◽  
Enhancement Method

The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

In situ acoustic estimates of the swimbladder volume of Atlantic herring (Clupea harengus)

2004 ◽  
Vol 61 (3) ◽  
pp. 323-337 ◽  
Author(s):  
Redwood W. Nero ◽  
Charles H. Thompson ◽  
J. Michael Jech
Keyword(s):  
Gulf Of Maine ◽  
Clupea Harengus ◽  
Sea Surface ◽  
Atlantic Herring ◽  
Acoustic Measurements ◽  
Neutral Buoyancy ◽  
Fish Weight ◽  
Neutrally Buoyant ◽  
Boyle’S Law

Abstract Acoustic measurements at 1.5–5 kHz on fish in the Gulf of Maine showed a swimbladder-resonance peak near 2.5 kHz at 160–190-m depth. Midwater trawls confirmed that the fish were likely to be Atlantic herring (Clupea harengus) of 19–29 cm length. Calculation using a model of swimbladder resonance gives swimbladder volumes of 1.2% of fish weight at 160–190 m. Extrapolation of this volume of gas using Boyle's Law suggests that at the sea surface, these herring would need to inflate their swimbladders by up to five to six times the volume required for neutral buoyancy. If these fish were to maintain this volume of gas with surface “gulping”, they would need to submerge from the sea surface with a 30% excess buoyancy. In general, swimbladders of the Clupeidae may have greater volumes of gas than if the fish were neutrally buoyant at the sea surface and the interpretation of HF-echosounder surveys may be additionally complex when the volume of gas and swimbladder volume are difficult to predict. Mechanisms of how herring obtain additional swimbladder gas are discussed.

Sign in / Sign up

Export Citation Format

Share Document