Fusion of adaptive algorithms for the classification of sea mines using high resolution side scan sonar in very shallow water

2002 ◽  
Author(s):  
T. Aridgides ◽  
M. Fernandez ◽  
G. Dobeck
Keyword(s):  
High Resolution ◽  
Shallow Water ◽  
Adaptive Algorithms ◽  
Side Scan Sonar

Shallow water High Resolution Bathymetric Side Scan Sonar

2007 ◽  
Author(s):  
Xiaodong Liu ◽  
Weiqing Zhu ◽  
Changle Fang ◽  
Wen Xu ◽  
Fangsheng Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Shallow Water ◽  
Side Scan Sonar

scholarly journals THE BOTTOM SUBSTRATE SHALLOW WATER MAPPING USING THE QUICK BIRD SATELLITE IMAGERY

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
Vincentius Siregar
Keyword(s):  
High Resolution ◽  
Shallow Water ◽  
Transformation Method ◽  
Live Coral ◽  
Bottom Substrate ◽  
Accuracy Test ◽  
Coral Sand ◽  
Water Mapping ◽  
Macro Algae

<p>The objective of this study was to explore the capability of high resolution satellite data of QuicBird to map the characteristics of the bottom shallow water (habitat) using the transformation method of two bands (blue and green) by implementing "depth invariant index" algorithm i.e., Y = ln Band 1 - (ki/kj) ln Band 2. The result provide more detail information on the characteristic of the bottom shallow water comparing to the used of original band (RGB). The classification of the transformed image showed 6 classes of bottom substrats i.e., Live coral, Death, Coral, Sand mix coral, Sand mix algae, and<br />Macro algae with Sand. The accuracy test of the map derived from the classification was about 79%.</p><p>Keywords: bottom shallow water, Quick Bird image, depth invariant index, classification</p>

scholarly journals THE BOTTOM SUBSTRATE SHALLOW WATER MAPPING USING THE QUICK BIRD SATELLITE IMAGERY

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
Vincentius Siregar
Keyword(s):  
High Resolution ◽  
Shallow Water ◽  
Satellite Imagery ◽  
Transformation Method ◽  
Live Coral ◽  
Bottom Substrate ◽  
Accuracy Test ◽  
Coral Sand ◽  
Water Mapping

The objective of this study was to explore the capability of high resolution satellite data of QuicBird to map the characteristics of the bottom shallow water (habitat) using the transformation method of two bands (blue and green) by implementing "depth invariant index" algorithm i.e., Y = ln Band 1 - (ki/kj) ln Band 2. The result provide more detail information on the characteristic of the bottom shallow water comparing to the used of original band (RGB). The classification of the transformed image showed 6 classes of bottom substrats i.e., Live coral, Death, Coral, Sand mix coral, Sand mix algae, andMacro algae with Sand. The accuracy test of the map derived from the classification was about 79%.Keywords: bottom shallow water, Quick Bird image, depth invariant index, classification

scholarly journals Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 29416-29428
Author(s):  
Xiaoming Qin ◽  
Xiaowen Luo ◽  
Ziyin Wu ◽  
Jihong Shang
Keyword(s):  
Deep Learning ◽  
Side Scan Sonar ◽  
Sediment Classification ◽  
Sonar Images ◽  
Small Side
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