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Image Optimization in Remote Sensing

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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 56228

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Dr. Diego Oliva

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Departamento de Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara 44430, Mexico
Interests: metaheuristic algorithms; bioinspired computation; image processing; machine learning; optimization
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Dr. Salvador Hinojosa

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

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Faculty of Computer Science and Engineering, Galala University, Suze 435611, Egypt
Interests: biomedical informatics; artificial intelligence; deep learning; machine learning; 6G networks; signal processing; internet of things
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Dr. Ahmed A. Ewees

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Department of Computer Sciences, Damietta University, Damietta Egypt
Interests: image processing; signal processing; machine learning; artificial intelligence; bio-inspired algorithms; text mining
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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 submissions that pass pre-check are 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 2700 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

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Published Papers (8 papers)

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Research

25 pages, 8678 KiB

Open AccessArticle

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

by Md Nazrul Islam, Murat Tahtali and Mark Pickering

Remote Sens. 2020, 12(11), 1776; https://doi.org/10.3390/rs12111776 - 1 Jun 2020

Cited by 8 | Viewed by 3826

Abstract

Multispectral Polarimetric Imagery (MSPI) contains significant information about an object’s distribution, shape, shading, texture and roughness features which can distinguish between foreground and background in a complex scene. Due to spectral signatures being limited to material properties, Background Segmentation (BS) is a difficult task when there are shadows, illumination and clutter in a scene. In this work, we propose a two-fold BS approach: multiband image fusion and polarimetric BS. Firstly, considering that the background in a scene is polarized by nature, the spectral reflectance and correlations and the textural features of MSPI are calculated and analyzed to demonstrate the fusion significance. After that, integrating Principal Component Analysis (PCA) with Fast Fourier Transform (FFT), a hybrid fusion technique is proposed to show the multiband fusion effectiveness. Secondly, utilizing the Stokes vector, polarimetric components are calculated to separate a complex scene’s background from its foreground by constructing four significant foreground masks. An intensity-invariant mask is built by differentiating between the median filtering versions of unpolarized and polarized images. A strongly unpolarized foreground mask is also constructed in two different ways, through analyzing the Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP). Moreover, a strongly polarized mask and a strong light intensity mask are also calculated based on the azimuth angle and the total light intensity. Finally, all these masks are combined, and a morphological operation is applied to segment the final background area of a scene. The proposed two-fold BS algorithm is evaluated using distinct statistical measurements and compared with well-known fusion methods and BS methods highlighted in this paper. The experimental results demonstrate that the proposed hybrid fusion method significantly improves multiband fusion quality. Furthermore, the proposed polarimetric BS approach also improves the mean accuracy, geometric mean and F1-score to 0.95, 0.93 and 0.97, respectively, for scenes in the MSPI dataset compared with those obtained from the methods in the literature considered in this paper. Future work will investigate mixed polarized and unpolarized BS in the MSPI dataset with specular reflection. Full article

(This article belongs to the Special Issue Image Optimization in Remote Sensing)

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35 pages, 5726 KiB

Open AccessArticle

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

by Heming Jia, Chunbo Lang, Diego Oliva, Wenlong Song and Xiaoxu Peng

Remote Sens. 2019, 11(12), 1421; https://doi.org/10.3390/rs11121421 - 14 Jun 2019

Cited by 176 | Viewed by 7418

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 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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27 pages, 10389 KiB

Open AccessArticle

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

by Hyunho Choi and Jechang Jeong

Remote Sens. 2019, 11(10), 1184; https://doi.org/10.3390/rs11101184 - 18 May 2019

Cited by 75 | Viewed by 12168

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, 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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19 pages, 2832 KiB

Open AccessArticle

Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

by Muhammad Ahmad, Asad Khan, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Ahmed Sohaib and Omar Nibouche

Remote Sens. 2019, 11(9), 1136; https://doi.org/10.3390/rs11091136 - 13 May 2019

Cited by 67 | Viewed by 5369

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 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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26 pages, 7429 KiB

Open AccessArticle

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

by Heming Jia, Chunbo Lang, Diego Oliva, Wenlong Song and Xiaoxu Peng

Remote Sens. 2019, 11(9), 1134; https://doi.org/10.3390/rs11091134 - 12 May 2019

Cited by 55 | Viewed by 7658

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 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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24 pages, 8926 KiB

Open AccessArticle

Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

by Heming Jia, Zhikai Xing and Wenlong Song

Remote Sens. 2019, 11(9), 1046; https://doi.org/10.3390/rs11091046 - 2 May 2019

Cited by 29 | Viewed by 4074

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 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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38 pages, 23681 KiB

Open AccessArticle

Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

by Heming Jia, Xiaoxu Peng, Wenlong Song, Diego Oliva, Chunbo Lang and Yao Li

Remote Sens. 2019, 11(8), 942; https://doi.org/10.3390/rs11080942 - 18 Apr 2019

Cited by 22 | Viewed by 4666

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 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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23 pages, 5153 KiB

Open AccessArticle

Estimating the Volume of Oil Tanks Based on High-Resolution Remote Sensing Images

by Tong Wang, Ying Li, Shengtao Yu and Yu Liu

Remote Sens. 2019, 11(7), 793; https://doi.org/10.3390/rs11070793 - 3 Apr 2019

Cited by 15 | Viewed by 9223

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 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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