Automatic Feature Detection of Ocean Internal Wave SAR Image Based on Hierarchical Clustering

2019 ◽  
Vol 09 (06) ◽  
pp. 1118-1125
Author(s):  
素芹 许
Keyword(s):  
Internal Wave ◽  
Hierarchical Clustering ◽  
Feature Detection ◽  
Sar Image

scholarly journals APPLICATION OF WAVELET TRANSFORM FOR INTERNAL WAVE DETECTION IN SAR IMAGE

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
YESSY ARVELYNA ◽  
MASAKI OSHIMA
Keyword(s):  
Wavelet Transform ◽  
Internal Wave ◽  
Feature Detection ◽  
Image Data ◽  
Spatial Location ◽  
Local Analysis ◽  
Sar Image ◽  
Kelvin Wave ◽  
Time Data ◽  
Wave Detection

In this paper, wavelet transform based on dyadic scales and spatial location have been used for internal wave detection in SAR image. It performed by using multi resolution analysis of image for feature detection and image enhancement. Wavelet transform uses local analysis to analyze a shorter region in image in time and scale data allows precise information than time and frequency region analysis such as Fourier analysis. Internal wave is observed in SAR image by effect of Bragg scattering process in sea surface that represents the meso-scale feature of sea processes. SAR image data is used considering to the effectiveness of large scales area monitoring on near real time data. Internal waves were observed in ERS-1 per 2 SAR images data over Lombok Strait during 1996-2001 period using 2D Symlet analysis for the symmetric extension of data at the image boundaries, to prevent discontinuities by a periodic wrapping of data in fast algorithm and space-saving code. Lombok Strait is chosen as study area because this strait is a major passage of the flow from Pacific Ocean to Indonesian seas (ARLINDO) and passage of Indian Ocean Kelvin wave to Makassar Strait. Keyword: SAR image, internal wave, wavelet analysis.

scholarly journals NON-TRIVIAL FEATURE DERIVATION FOR INTENSIFYING FEATURE DETECTION USING LIDAR DATASETS THROUGH ALLOMETRIC AGGREGATION DATA ANALYSIS APPLYING DIFFUSED HIERARCHICAL CLUSTERING FOR DISCRIMINATING AGRICULTURAL LAND COVER IN PORTIONS OF NORTHERN MINDANAO, PHILIPPINES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1009-1016
Author(s):  
Ricardo G. Villar ◽  
Jigg L. Pelayo ◽  
Ray Mari N. Mozo ◽  
James B. Salig Jr. ◽  
Jojemar Bantugan
Keyword(s):  
Data Analysis ◽  
Hierarchical Clustering ◽  
Feature Detection ◽  
Agricultural Land ◽  
Feature Space ◽  
Machine Learning Algorithms ◽  
Kappa Index ◽  
Cloud Data ◽  
Allometric Relations ◽  
Novel Approach

Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR) derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m<sup>2</sup> of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc.) in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification in this investigation was compared to a classification based on conventional object-oriented approach promoting straight-forward functionalities of the software eCognition. A compelling rise of efficiency in the overall accuracy (74.4% to 93.4%) and kappa index of agreement (70.5% to 91.7%) is noticeable based on the initial process. Nevertheless, having low-dense LiDAR dataset could be enough in generating exponential increase of performance in accuracy.

Study on extracting and verifying internal wave parameter of SAR image

2015 ◽  
Author(s):  
Juan Wang ◽  
Jingsong Yang ◽  
Junde Li ◽  
Lin Ren ◽  
Gang Zheng
Keyword(s):  
Internal Wave ◽  
Wave Parameter ◽  
Sar Image

Internal wave parameters retrieval from space-borne SAR image

2015 ◽  
Vol 9 (4) ◽  
pp. 700-708 ◽  
Author(s):  
Kaiguo Fan ◽  
Bin Fu ◽  
Yanzhen Gu ◽  
Xingxiu Yu ◽  
Tingting Liu ◽  
...  
Keyword(s):  
Internal Wave ◽  
Sar Image ◽  
Wave Parameters

Semi-supervised Hierarchical Clustering for Semantic SAR Image Annotation

2016 ◽  
Vol 9 (5) ◽  
pp. 1993-2008 ◽  
Author(s):  
Wei Yao ◽  
Corneliu Octavian Dumitru ◽  
Otmar Loffeld ◽  
Mihai Datcu
Keyword(s):  
Hierarchical Clustering ◽  
Image Annotation ◽  
Sar Image

Automatic detection of ocean internal wave from SAR image

2011 ◽  
Author(s):  
Kaiguo Fan ◽  
Weigen Huang ◽  
Peng Chen ◽  
Xingxiu Yu ◽  
Bin Fu ◽  
...  
Keyword(s):  
Internal Wave ◽  
Automatic Detection ◽  
Sar Image
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