Application of wavelet transforms to detection, estimation, modeling, and remote sensing in underwater acoustics

1998 ◽  
Vol 104 (3) ◽  
pp. 1760-1760
Author(s):  
Leon H. Sibul ◽  
Michael J. Roan
Keyword(s):  
Remote Sensing ◽  
Wavelet Transforms ◽  
Underwater Acoustics

scholarly journals Remote Sensing of Volcanic Processes and Risk

2020 ◽  
Vol 12 (16) ◽  
pp. 2567
Author(s):  
Francesca Cigna ◽  
Deodato Tapete ◽  
Zhong Lu
Keyword(s):  
Remote Sensing ◽  
Case Studies ◽  
Wavelet Transforms ◽  
Ground Deformation ◽  
Volcanic Eruptions ◽  
Added Value ◽  
Landsat 8 ◽  
Magnetic Survey ◽  
Special Issue ◽  
Volcanic Processes

Remote sensing data and methods are increasingly being embedded into assessments of volcanic processes and risk. This happens thanks to their capability to provide a spectrum of observation and measurement opportunities to accurately sense the dynamics, magnitude, frequency, and impacts of volcanic activity in the ultraviolet (UV), visible (VIS), infrared (IR), and microwave domains. Launched in mid-2018, the Special Issue “Remote Sensing of Volcanic Processes and Risk” of Remote Sensing gathers 19 research papers on the use of satellite, aerial, and ground-based remote sensing to detect thermal features and anomalies, investigate lava and pyroclastic flows, predict the flow path of lahars, measure gas emissions and plumes, and estimate ground deformation. The strong multi-disciplinary character of the approaches employed for volcano monitoring and the combination of a variety of sensor types, platforms, and methods that come out from the papers testify the current scientific and technology trends toward multi-data and multi-sensor monitoring solutions. The research advances presented in the published papers are achieved thanks to a wealth of data including but not limited to the following: thermal IR from satellite missions (e.g., MODIS, VIIRS, AVHRR, Landsat-8, Sentinel-2, ASTER, TET-1) and ground-based stations (e.g., FLIR cameras); digital elevation/surface models from airborne sensors (e.g., Light Detection And Ranging (LiDAR), or 3D laser scans) and satellite imagery (e.g., tri-stereo Pléiades, SPOT-6/7, PlanetScope); airborne hyperspectral surveys; geophysics (e.g., ground-penetrating radar, electromagnetic induction, magnetic survey); ground-based acoustic infrasound; ground-based scanning UV spectrometers; and ground-based and satellite Synthetic Aperture Radar (SAR) imaging (e.g., TerraSAR-X, Sentinel-1, Radarsat-2). Data processing approaches and methods include change detection, offset tracking, Interferometric SAR (InSAR), photogrammetry, hotspots and anomalies detection, neural networks, numerical modeling, inversion modeling, wavelet transforms, and image segmentation. Some authors also share codes for automated data analysis and demonstrate methods for post-processing standard products that are made available for end users, and which are expected to stimulate the research community to exploit them in other volcanological application contexts. The geographic breath is global, with case studies in Chile, Peru, Ecuador, Guatemala, Mexico, Hawai’i, Alaska, Kamchatka, Japan, Indonesia, Vanuatu, Réunion Island, Ethiopia, Canary Islands, Greece, Italy, and Iceland. The added value of the published research lies on the demonstration of the benefits that these remote sensing technologies have brought to knowledge of volcanoes that pose risk to local communities; back-analysis and critical revision of recent volcanic eruptions and unrest periods; and improvement of modeling and prediction methods. Therefore, this Special Issue provides not only a collection of forefront research in remote sensing applied to volcanology, but also a selection of case studies proving the societal impact that this scientific discipline can potentially generate on volcanic hazard and risk management.

scholarly journals Change Detection Algorithm Based on Discrete Wavelet Transforms and Neighborhood Fuzzy C-Means

2018 ◽  
Vol 36 (3) ◽  
pp. 426-431
Author(s):  
Fengping Wang ◽  
Weixing Wang ◽  
Ting Gao ◽  
Weiwei Chen ◽  
Hongxia Li
Keyword(s):  
Remote Sensing ◽  
Wavelet Transforms ◽  
Detection Probability ◽  
Detection Algorithm ◽  
Spatial Distance ◽  
Ratio Method ◽  
Discrete Wavelet ◽  
Forest Change ◽  
Distance Information ◽  
Ratio Image

A new algorithm on Discrete Wavelet Transform (DWT) and neighborhood FCM is proposed to detect change area from remote sensing image. First, the subtraction and ratio image are obtained by the subtraction and ratio method from the two registered remote sensing images; Then, the DWT is applied to the subtraction and ratio image, the region intensity-based and energy-based fusion rules is adopted to the low frequency and high frequency wavelet coefficients, and the inverse DWT is used to obtain the final difference image; At last, the neighborhood FCM is carried out to get the change areas, the spatial distance information and gray difference information are considered in the objective function of FCM, which could avoid misclassification and enhance the detection probability. Experimental results show that the proposed algorithm has strong ability to suppress noise and good detection results; the detection probability of unban change area can reach to 98.45%, whereas, the detection probability is up to 87.5% for the discontinuous forest change area.

scholarly journals Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
K. Parvathi ◽  
B. S. Prakasa Rao ◽  
M. Mariya Das ◽  
T. V. Rao
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
High Resolution ◽  
Wavelet Transforms ◽  
Intensity Variation ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Watershed Segmentation ◽  
Remote Sensing Images ◽  
Grey Scale

The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal) and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

scholarly journals Investigation of the basic Haar wavelet-transformations in the problem of decryption of space images on detection of waste disposal fields

2018 ◽  
Vol 30 ◽  
pp. 103-122
Author(s):  
Maretta Kazaryan ◽  
Mikhail Shahramanian ◽  
Svetoslav Zabunov
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Wavelet Transforms ◽  
Experimental Studies ◽  
Haar Wavelet ◽  
Wavelet Expansion ◽  
Two Dimensional ◽  
Space Images ◽  
The Earth ◽  
Wavelet Transformations

In this paper, we study the use of orthogonal transformations, namely, the basic Haar wavelet transforms, for data processing of the Earth remote sensing. The internal structure of orthogonal Haar transforms is considered. The Haar matrix is divided into blocks of the same type, so that parallelization of the computations is possible. The expediency of replacing the spectral components corresponding to the whole block (or several blocks) of the original matrix with zeros is asserted. Theoretical and experimental studies are carried out to improve the results of image classification (on the example of cluster analysis). The Haar wavelet expansion coefficients are used as indicators when decoding space images for the presence of waste disposal sites. The aim of this paper is to describe the approach, on the basis of which an optimal method is established on a class of vectors with real components, application of two-dimensional discrete Haar wavelet transformations in the problem of recognition of space images for the presence of waste disposal sites. General methodology of research. The paper uses elements of mathematical analysis, wavelet analysis, the theory of discrete orthogonal transformations, and methods for decoding cosmic images. Scientific novelty. Encoding by means of conversion is an indirect method, especially effective in processing of two-dimensional signals, in particular, space images used for remote sensing of the Earth. We propose the approach that takes into account the structure of the wavelet-Haar matrix, while recognizing waste disposal fields by means of space images. The article comprises the result of the experimental application of wavelet-Haar transformations for decoding of space images. We consider this case, both with and without the technique of taking into account the structure of the wavelet-Haar matrices.

scholarly journals An Efficient Image Compressor for Charge Coupled Devices Camera

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Jin Li ◽  
Fei Xing ◽  
Zheng You
Keyword(s):  
Remote Sensing ◽  
Wavelet Transforms ◽  
Ccd Camera ◽  
Low Complexity ◽  
Remote Sensing Image ◽  
Discrete Wavelet ◽  
Bit Rate ◽  
Compression Scheme ◽  
Low Bit Rate ◽  
Charge Coupled Devices

Recently, the discrete wavelet transforms- (DWT-) based compressor, such as JPEG2000 and CCSDS-IDC, is widely seen as the state of the art compression scheme for charge coupled devices (CCD) camera. However, CCD images project on the DWT basis to produce a large number of large amplitude high-frequency coefficients because these images have a large number of complex texture and contour information, which are disadvantage for the later coding. In this paper, we proposed a low-complexity posttransform coupled with compressing sensing (PT-CS) compression approach for remote sensing image. First, the DWT is applied to the remote sensing image. Then, a pair base posttransform is applied to the DWT coefficients. The pair base are DCT base and Hadamard base, which can be used on the high and low bit-rate, respectively. The best posttransform is selected by thelp-norm-based approach. The posttransform is considered as the sparse representation stage of CS. The posttransform coefficients are resampled by sensing measurement matrix. Experimental results on on-board CCD camera images show that the proposed approach significantly outperforms the CCSDS-IDC-based coder, and its performance is comparable to that of the JPEG2000 at low bit rate and it does not have the high excessive implementation complexity of JPEG2000.

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