Special Issue "Wavelet Transform for Remote Sensing Image Analysis"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 May 2022.

Special Issue Editors

Dr. Sathishkumar Samiappan
E-Mail Website
Guest Editor
Geosystems Research Institute, Mississippi State University, 2 Research Boulevard Starkville, MS 39759, USA
Interests: machine learning; pattern recognition; signal processing; remote sensing
Prof. Dr. Balakrishna Gokaraju
E-Mail Website
Guest Editor
Computational Science & Engineering, North Carolina A&T University, 1601 E. Market Street, Greensboro, NC 27411, USA
Interests: remote sesnsing; machine learning; image analysis

Special Issue Information

Dear Colleagues,

Wavelet transform is a popular approach in signal and image processing, with wide-ranging applications including remote sensing. Wavelet transforms start with an orthogonal basis of constant functions, constructed by simple dilation and translation to map any function to its coefficients with respect to this basis. When this transformation is applied to remote sensing images, the resulting coefficients can be used to solve numerous problems in this field ranging from feature extraction, compression, and registration to advanced applications such as extracting bathymetry from radars.

This Special Issue is focused on the use of wavelet transforms for remote sensing images in order to cover the broad range of possibilities. Theoretical, methodological, experimental, and application papers are welcome addressing (but not limited to) the following aspects:

  • Feature extraction—multispectral, hyperspectral, and radar;
  • Image enhancement and noise reduction;
  • Super-resolution reconstruction;
  • Image change detection;
  • Image fusion;
  • Image registration;
  • Image compression;
  • Applications in high-resolution images collected from small drones.

Dr. Sathishkumar Samiappan
Prof. Dr. Balakrishna Gokaraju
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • feature extraction
  • compression
  • fusion
  • registration
  • super-resolution
  • noise reduction

Published Papers (3 papers)

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Research

Article
Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy
Remote Sens. 2021, 13(19), 3847; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193847 (registering DOI) - 26 Sep 2021
Abstract
The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in [...] Read more.
The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area. Full article
(This article belongs to the Special Issue Wavelet Transform for Remote Sensing Image Analysis)
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Article
Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform
Remote Sens. 2021, 13(7), 1255; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071255 - 25 Mar 2021
Cited by 1 | Viewed by 658
Abstract
Hyperspectral image classification is an emerging and interesting research area that has attracted several researchers to contribute to this field. Hyperspectral images have multiple narrow bands for a single image that enable the development of algorithms to extract diverse features. Three-dimensional discrete wavelet [...] Read more.
Hyperspectral image classification is an emerging and interesting research area that has attracted several researchers to contribute to this field. Hyperspectral images have multiple narrow bands for a single image that enable the development of algorithms to extract diverse features. Three-dimensional discrete wavelet transform (3D-DWT) has the advantage of extracting the spatial and spectral information simultaneously. Decomposing an image into a set of spatial–spectral components is an important characteristic of 3D-DWT. It has motivated us to perform the proposed research work. The novelty of this work is to bring out the features of 3D-DWT applicable to hyperspectral images classification using Haar, Fejér-Korovkin and Coiflet filters. Three-dimensional-DWT is implemented with the help of three stages of 1D-DWT. The first two stages of 3D-DWT are extracting spatial resolution, and the third stage is extracting the spectral content. In this work, the 3D-DWT features are extracted and fed to the following classifiers (i) random forest (ii) K-nearest neighbor (KNN) and (iii) support vector machine (SVM). Exploiting both spectral and spatial features help the classifiers to provide a better classification accuracy. A comparison of results was performed with the same classifiers without DWT features. The experiments were performed using Salinas Scene and Indian Pines hyperspectral datasets. From the experiments, it has been observed that the SVM with 3D-DWT features performs better in terms of the performance metrics such as overall accuracy, average accuracy and kappa coefficient. It has shown significant improvement compared to the state of art techniques. The overall accuracy of 3D-DWT+SVM is 88.3%, which is 14.5% larger than that of traditional SVM (77.1%) for the Indian Pines dataset. The classification map of 3D-DWT + SVM is more closely related to the ground truth map. Full article
(This article belongs to the Special Issue Wavelet Transform for Remote Sensing Image Analysis)
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Article
Compression of Remotely Sensed Astronomical Image Using Wavelet-Based Compressed Sensing in Deep Space Exploration
Remote Sens. 2021, 13(2), 288; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020288 - 15 Jan 2021
Cited by 1 | Viewed by 595
Abstract
Compression of remotely sensed astronomical images is an essential part of deep space exploration. This study proposes a wavelet-based compressed sensing (CS) algorithm for astronomical image compression in a miniaturized independent optical sensor system, which introduces a new framework for CS in the [...] Read more.
Compression of remotely sensed astronomical images is an essential part of deep space exploration. This study proposes a wavelet-based compressed sensing (CS) algorithm for astronomical image compression in a miniaturized independent optical sensor system, which introduces a new framework for CS in the wavelet domain. The algorithm starts with a traditional 2D discrete wavelet transform (DWT), which provides frequency information of an image. The wavelet coefficients are rearranged in a new structured manner determined by the parent–child relationship between the sub-bands. We design scanning modes based on the direction information of high-frequency sub-bands, and propose an optimized measurement matrix with a double allocation of measurement rate. Through a single measurement matrix, higher measurement rates can be simultaneously allocated to sparse vectors containing more information and coefficients with higher energy in sparse vectors. The double allocation strategy can achieve better image sampling. At the decoding side, orthogonal matching pursuit (OMP) and inverse discrete wavelet transform (IDWT) are used to reconstruct the image. Experimental results on simulated image and remotely sensed astronomical images show that our algorithm can achieve high-quality reconstruction with a low measurement rate. Full article
(This article belongs to the Special Issue Wavelet Transform for Remote Sensing Image Analysis)
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