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Special Issue "Image Optimization in Remote Sensing"

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

Deadline for manuscript submissions: 30 September 2019

Special Issue Editors

Guest Editor
Dr. Diego Oliva

Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jalisco, México
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Interests: Image processing, real-world implementations of image processing, optimization, metaheuristic algorithms, multiobjective optimization, machine learning, neural networks
Guest Editor
Dr. Salvador Hinojosa

Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense de Madrid, José Santesmases Str, 9, 28040 Madrid, Spain
Website | E-Mail
Interests: Image processing, optimization, metaheuristic algorithms, multiobjective optimization, machine learning, neural networks
Guest Editor
Dr. Mohamed Abd Elaziz

Department of Mathematics, Faculty of Science, Zagazig University, Shaibet an Nakareyah, Markaz El-Zakazik, Oriental 44519, Zagazig, Egypt
Website | E-Mail
Interests: Image processing, signal processing, optimization, metaheuristic algorithms, multiobjective optimization, machine learning
Guest Editor
Dr. Ahmed A. Ewees

Department of Computer Sciences, Damietta University, Damietta Egypt
E-Mail
Interests: Image processing, signal processing, machine learning, artificial intelligence, bio-inspired algorithms, text mining

Special Issue Information

Dear Colleagues,

Remote sensing is defined as the science of analyzing and monitoring physical characteristics of an area with the measurement of its reflected or emitted radiation.  Typically, remote sensing information is obtained from airplanes or satellites at a great distance from the surface of the earth, enabling regular monitoring of land, ocean, and atmospheric conditions for multiple applications, such as mineralogy, biology, defense, and environmental preservation.

The data acquired for remote sensing can be represented in the form of images to make its analysis easier. However, such images present interesting characteristics such as a high spectral-spatial-temporal resolution, and multiple channels that provide valuable information independently or all together.  These facts generate a big amount of information that must be properly and accurately analyzed. Some of the issues related to images from remote sensing applications can be treated as optimization problems. Thus, the necessity to design and implement optimization methods that possess a superior performance on the search for optimal solutions for remote sensing applications arises.

This special issue concerns the implementation and development of optimization techniques able to find the best solutions for processing remote sensing images. In general, in this special issue the latest advances and trends of optimization algorithms for remote sensing image processing will be presented, addressing original developments, new applications, and practical solutions to open questions. The aim is to increase the data and knowledge exchange between the optimization and remote sensing communities and allow experts from other areas to understand the inherent problematics of remote sensing. Moreover, authors are encouraged to present hybrid methods that might include the use of machine learning approaches.

The topics for this Special Issue include, but are not limited to, the following:

  • 3D radar and 3D sonar imaging
  • Sonar image processing, data reduction, feature extraction, and image understanding
  • Interferometric methods
  • Sparse image reconstruction
  • Hyperspectral images
  • Object extraction and accuracy evaluation in 3D reconstruction
  • Satellite images
  • Surveillance systems
  • Multi-sensor data fusion
  • Image segmentation
  • Multilevel thresholding
  • Clustering
  • Metaheuristic Algorithms
  • Classical optimization techniques
  • Hybrid optimization mechanisms
  • Machine learning
  • Fuzzy logic approaches
  • Neural computing
  • Evolutionary computation
  • Multi-objective optimization
  • Many-objective optimization
  • Hyper-heuristics
  • Heuristics
  • Swarm algorithms
  • Feature selection

Dr. Diego Oliva
Dr. Salvador Hinojosa
Dr. Mohamed Abd Elaziz
Dr. Ahmed A. Ewees
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 1800 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

  • Remote sensing images
  • Image processing
  • Global optimization
  • Multi-objective optimization
  • Hybrid optimization algorithms
  • Machine learning approaches

Published Papers (7 papers)

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Research

Open AccessArticle
Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation
Remote Sens. 2019, 11(12), 1421; https://doi.org/10.3390/rs11121421
Received: 2 May 2019 / Revised: 10 June 2019 / Accepted: 12 June 2019 / Published: 14 June 2019
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Abstract
In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid [...] Read more.
In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests. Full article
(This article belongs to the Special Issue Image Optimization in Remote Sensing)
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Graphical abstract

Open AccessArticle
Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform
Remote Sens. 2019, 11(10), 1184; https://doi.org/10.3390/rs11101184
Received: 2 May 2019 / Revised: 14 May 2019 / Accepted: 14 May 2019 / Published: 18 May 2019
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Abstract
Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, [...] Read more.
Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity. Full article
(This article belongs to the Special Issue Image Optimization in Remote Sensing)
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Open AccessArticle
Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images
Remote Sens. 2019, 11(9), 1136; https://doi.org/10.3390/rs11091136
Received: 26 March 2019 / Revised: 28 April 2019 / Accepted: 5 May 2019 / Published: 13 May 2019
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Abstract
Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach [...] Read more.
Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements. Full article
(This article belongs to the Special Issue Image Optimization in Remote Sensing)
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Open AccessArticle
Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation
Remote Sens. 2019, 11(9), 1134; https://doi.org/10.3390/rs11091134
Received: 23 April 2019 / Accepted: 10 May 2019 / Published: 12 May 2019
Cited by 1 | PDF Full-text (7429 KB) | HTML Full-text | XML Full-text
Abstract
An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search [...] Read more.
An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method. Full article
(This article belongs to the Special Issue Image Optimization in Remote Sensing)
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Graphical abstract

Open AccessArticle
Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation
Remote Sens. 2019, 11(9), 1046; https://doi.org/10.3390/rs11091046
Received: 21 April 2019 / Accepted: 30 April 2019 / Published: 2 May 2019
Cited by 1 | PDF Full-text (8926 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the [...] Read more.
This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background. Full article
(This article belongs to the Special Issue Image Optimization in Remote Sensing)
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Open AccessArticle
Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm
Remote Sens. 2019, 11(8), 942; https://doi.org/10.3390/rs11080942
Received: 3 April 2019 / Revised: 13 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
Cited by 1 | PDF Full-text (23681 KB) | HTML Full-text | XML Full-text
Abstract
A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation [...] Read more.
A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon’s rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness. Full article
(This article belongs to the Special Issue Image Optimization in Remote Sensing)
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Open AccessArticle
Estimating the Volume of Oil Tanks Based on High-Resolution Remote Sensing Images
Remote Sens. 2019, 11(7), 793; https://doi.org/10.3390/rs11070793
Received: 1 March 2019 / Revised: 31 March 2019 / Accepted: 1 April 2019 / Published: 3 April 2019
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Abstract
The purpose of this study is to obtain oil tank volumes from high-resolution satellite imagery to meet the need to measure oil tank volume globally. A preprocessed remote sensing HSV image is used to extract the shadow of the oil tank by Otsu [...] Read more.
The purpose of this study is to obtain oil tank volumes from high-resolution satellite imagery to meet the need to measure oil tank volume globally. A preprocessed remote sensing HSV image is used to extract the shadow of the oil tank by Otsu thresholding, shadow area thresholding, and morphological closing. The oil tank shadow is crescent-shaped. Hence, a median method based on sub-pixel subdivision positioning is used to calculate the shadow length of the oil tank and then determine its height with high precision. The top of the tank and its radius in the image are identified using the Hough transform. The final tank volume is calculated using its height and radius. A high-resolution Gaofen 2 optical remote sensing image is used to evaluate the proposed method. The actual height and volume of the tank we tested were 21.8 m and 109,532 m3. The experimental results show that the mean absolute error of the height of the tank calculated by the median method is 0.238 m, the relative error is within 1.15%, and the RMES is 0.23. The result is better than the previous work. The absolute error between the calculated and the actual tank volumes ranges between 416 and 3050 m3, and the relative error ranges between 0.38% and 2.78%. These results indicate that the proposed method can calculate the volume of oil tanks with high precision and sufficient accuracy for practical applications. Full article
(This article belongs to the Special Issue Image Optimization in Remote Sensing)
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