Browsing by Author "Suresh, S."

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A continuous time model for Karnatic flute music synthesis
(Cogent OA, 2023) Rajan, R.M.; Vijayasenan, D.; Suresh, S.
Gamakas, the essential embellishments, are integral parts of Karnatic music. Synthesising any form of Karnatic music necessitates proper modelling and synthesis of different gamakas associated with each note. We propose a spectral model to efficiently synthesize gamakas for Karnatic bamboo flute music from the notes, duration and gamaka information. We model three different components of the flute sound, namely, pitch contour, harmonic weights and time domain amplitude envelope. Cubic splines are used to parametrically represent these components. Subjective analysis of the results shows that the proposed method is better than the existing spectral methods in terms of tonal and aesthetic qualities of gamaka rendition. Hypothesis test results show that the observed improvements over other methods are statistically significant at 95% confidence interval. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
A Framework for Quality Enhancement of Multispectral Remote Sensing Images
(Institute of Electrical and Electronics Engineers Inc., 2018) Suresh, S.; Das, D.; Lal, S.
Researches in satellite image enhancement have been particularly confined to two major areas-contrast enhancement and image de noising of remote sensing images. The processing of relatively dark or shadowed images necessitates the need for robust remote sensing enhancement techniques. In this paper, a robust framework for quality enhancement of multispectral remote sensing images is proposed. The quantitative results of proposed algorithm and other existing remote sensing enhancement algorithms are calculated in terms of DE, NIQMC, BIQME, PisDist and CM on different remote sensing and other image databases. Results reveal that visual enhancement of the proposed algorithm is better than other existing remote sensing enhancement algorithms. Finally, the simulation experimental results show that proposed algorithm is effective and efficient for remotes sensing as well as natural images. Â© 2017 IEEE.
A metaheuristic framework based automated Spatial-Spectral graph for land cover classification from multispectral and hyperspectral satellite images
(Elsevier B.V., 2020) Suresh, S.; Lal, S.
Land cover classification of satellite images has been a very predominant area since the last few years. An increase in the amount of information acquired by satellite imaging systems, urges the need for automatic tools for classification. Satellite images exhibit spatial and/or temporal dependencies in which the conventional machine learning algorithms fail to perform well. In this paper, we propose an improved framework for automated land cover classification using Spatial Spectral Schroedinger Eigenmaps (SSSE) optimized by Cuckoo Search (CS) algorithm. Support Vector Machine (SVM) is adopted for the final thematic map generation following dimensionality reduction and clustering by the proposed approach. The novelty of the proposed framework is that the applicability of optimized SSSE for land cover classification of medium and high resolution multi-spectral satellite images is tested for the first time. The proposed method makes land cover classification system fully automatic by optimizing the algorithm specific image dependent parameter ? using CS algorithm. Experiments are carried out over publicly available high and medium resolution multi-spectral satellite image datasets (Landsat 5 TM and IKONOS 2 MS) and hyper-spectral satellite image datasets (Pavia University and Indian Pines) to assess the robustness of the proposed approach. Performance comparisons of the proposed method against state-of-the-art multi-spectral and hyper-spectral land cover classification methods reveal the efficiency of the proposed method. © 2020 Elsevier B.V.
A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images
(Institute of Electrical and Electronics Engineers, 2017) Suresh, S.; Lal, S.; Chintala, C.S.; Kiran, M.S.
Owing to the increased demand for satellite images for various practical applications, the use of proper enhancement methods are inevitable. Visual enhancement of such images mainly focuses on improving the contrast of the scene procured, conserving its naturalness with minimum image artifacts. Last one decade traced an extensive use of metaheuristic approaches for automatic image enhancement processes. In this paper, a robust and novel adaptive Cuckoo search based Enhancement algorithm is proposed for the enhancement of various satellite images. The proposed algorithm includes a chaotic initialization phase, an adaptive Levy flight strategy and a mutative randomization phase. Performance evaluation is done by quantitative and qualitative results comparison of the proposed algorithm with other state-of-the-art metaheuristic algorithms. Box-and-whisker plots are also included for evaluating the stability and convergence capability of all the algorithms tested. Test results substantiate the efficiency and robustness of the proposed algorithm in enhancing a wide range of satellite images. © 2008-2012 IEEE.
A robust framework for quality enhancement of aerial remote sensing images
(Elsevier B.V., 2018) Karuna Kumari, E.; Das, D.; Suresh, S.; Lal, S.; Narasimhadhan, A.V.
This paper proposes a robust framework for quality restoration of remotely sensed aerial images. Proposed framework works in three steps: (1) Efficient color balancing and saturation adjustment, (2) Efficient color restoration, (3) Modified contrast enhancement using particle swarm optimization (PSO). In order to show the robustness, step-wise results of proposed framework is illustrated. Several aerial images from two publically available datasets are tested to support the robustness of the proposed framework over existing image quality restoration methods. The experimental results of proposed framework and other existing quality restoration methods are compared in terms of NIQMC, BIQME, MICHELSON, DE, EME and PIXDIST along with visual experimental results. Based on experimental results conducted on several aerial images suggest that the proposed framework is outperform over existing quality restoration methods. © 2018 Elsevier B.V.
Adsorption of benzene vapor onto activated biomass from cashew nut shell: Batch and column study
(Bentham Science Publishers, 2012) Suresh, S.; Vijayalakshmi, G.; Rajmohan, B.; Subbaramaiah, V.
The preparation of chemically modified activated cashew nut shell (ACNSB) of different impregnation ratios and their effects in adsorption of benzene vapor were studied. Effects of chemical pre-impregnation using phosphoric acid at different ratios (1:1 and 2:1) were investigated in order to patent. Physico-chemical characterization including surface area, scanning electron microscopy, energy dispersive X-ray spectroscopy, High-resolution Transmission Electron Microscopy and Fourier transform infrared spectroscopy of the ACNSB before and after benzene adsorption have been done to understand the adsorption mechanism. Optimum conditions for benzene removal were found to be, adsorbent dose m=10 g/l of solution and time (t) 120 min for the C0 range of 300-500 mg/l. Adsorption of benzene followed pseudosecond-order kinetics. Langmuir and R-P isotherms were found to best represented data for benzene adsorption onto ACSNB. In ACNSB column experiments, it can be concluded that concentration of benzene increases with the longer breakthrough time and hence higher adsorption capacity. ACSNB are many advantages includes simple and fast, organic solvent recovery, economical, energy savings, environmentally safe aspect and minimize the waste management problem. © 2012 Bentham Science Publishers.
An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions
(Elsevier Ltd, 2016) Suresh, S.; Lal, S.
Satellite image segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. Several bio-inspired algorithms were developed to generate optimum threshold values for segmenting such images efficiently. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding. In this paper, we propose a computationally efficient image segmentation algorithm, called CSMcCulloch, incorporating McCulloch's method for lévy flight generation in Cuckoo Search (CS) algorithm. We have also investigated the impact of Mantegna?s method forlévy flight generation in CS algorithm (CSMantegna) by comparing it with the conventional CS algorithm which uses the simplified version of the same. CSMantegna algorithm resulted in improved segmentation quality with an expense of computational time. The performance of the proposed CSMcCulloch algorithm is compared with other bio-inspired algorithms such as Particle Swarm Optimization (PSO) algorithm, Darwinian Particle Swarm Optimization (DPSO) algorithm, Artificial Bee Colony (ABC) algorithm, Cuckoo Search (CS) algorithm and CSMantegna algorithm using Otsu's method, Kapur entropy and Tsallis entropy as objective functions. Experimental results were validated by measuring PSNR, MSE, FSIM and CPU running time for all the cases investigated. The proposed CSMcCulloch algorithm evolved to be most promising, and computationally efficient for segmenting satellite images. Convergence rate analysis also reveals that the proposed algorithm outperforms others in attaining stable global optimum thresholds. The experiments results encourages related researches in computer vision, remote sensing and image processing applications. © 2016 Elsevier Ltd. All rights reserved.
Damage identification and assessment using image processing on post-disaster satellite imagery
(2017) Joshi, A.R.; Tarte, I.; Suresh, S.; Koolagudi, S.G.
Natural disasters such as earthquakes and tsunamis often have a devastating effect on human life and cause noticeable damage to infrastructure. Active research has been ongoing to mitigate the impact of these catastrophes and preclude the economic losses. The existing methods that utilize pre-event and post-event images not only require the immediate and guaranteed availability of the appropriate data set but are also encumbered by manual mapping of the images, necessitating the indication of corresponding control points in the two images. This paper highlights the use of only post-event imagery in the absence of reference data to achieve a more timely delivery to produce damage maps as the output. This eliminates the need for manual georeferencing of images. Our method incorporates simple linear iterative clustering (SLIC) for segmenting the images into uniform superpixels and extraction of 62 features for each superpixel. We used various classifiers of which Random Forest classifier was found to give a comparatively high accuracy of 90.4% over others. To enumerate the accuracy of the method proposed, we used 1500 data regions of which 20% were used for testing, and 80% were used for training. The aerial images taken by GeoEye1 after the 2011 Christchurch earthquake and 2011 Japan earthquake and tsunami are utilized in this study to detect building damage. In the case of availability of ground truth, we compare the histograms of the pre- and post-imagery to quantify similarity as the SSD (Sum of Squared Distances) value and thus, our approach produces an assessment as an output map displaying the extent of damage in the area covered by each superpixel. We consider 6 levels of damage ranging from 1 to 6, where 1 signifies no damage, and 6, maximum damage. � 2017 IEEE.
Damage identification and assessment using image processing on post-disaster satellite imagery
(Institute of Electrical and Electronics Engineers Inc., 2017) Joshi, A.R.; Tarte, I.; Suresh, S.; Koolagudi, S.G.
Natural disasters such as earthquakes and tsunamis often have a devastating effect on human life and cause noticeable damage to infrastructure. Active research has been ongoing to mitigate the impact of these catastrophes and preclude the economic losses. The existing methods that utilize pre-event and post-event images not only require the immediate and guaranteed availability of the appropriate data set but are also encumbered by manual mapping of the images, necessitating the indication of corresponding control points in the two images. This paper highlights the use of only post-event imagery in the absence of reference data to achieve a more timely delivery to produce damage maps as the output. This eliminates the need for manual georeferencing of images. Our method incorporates simple linear iterative clustering (SLIC) for segmenting the images into uniform superpixels and extraction of 62 features for each superpixel. We used various classifiers of which Random Forest classifier was found to give a comparatively high accuracy of 90.4% over others. To enumerate the accuracy of the method proposed, we used 1500 data regions of which 20% were used for testing, and 80% were used for training. The aerial images taken by GeoEye1 after the 2011 Christchurch earthquake and 2011 Japan earthquake and tsunami are utilized in this study to detect building damage. In the case of availability of ground truth, we compare the histograms of the pre- and post-imagery to quantify similarity as the SSD (Sum of Squared Distances) value and thus, our approach produces an assessment as an output map displaying the extent of damage in the area covered by each superpixel. We consider 6 levels of damage ranging from 1 to 6, where 1 signifies no damage, and 6, maximum damage. Â© 2017 IEEE.
Dehazing of Satellite Images using Adaptive Black Widow Optimization-based framework
(Taylor and Francis Ltd., 2021) Suresh, S.; Rajan, M.; Pushparaj, J.; Cs, A.; Lal, S.; Chintala, C.S.
Haze is a common atmospheric disturbance that adversely affects the quality of optical data, thus often restricting their usability. Since these effects are inherent in the process of spaceborne Earth sensing, it is important to develop effective methods to remove them. This work proposes a novel method for de-hazing satellite imagery and outdoor camera images. It is developed by modifying the transmission map used in Dark Channel Prior (DCP) method. A Weighted Variance Guided Filter (WVGF) is introduced for enhancing the image quality, which included a two-stage image decomposition and fusion process. The method also optimally combines the radiance and transmission components along with an additional stage modelling a fusion-based transparency function. A final guided filter-based image refinement scheme is incorporated to improve the processed image quality. The optimal tuning of the image-dependent parameters at various stages is achieved using the newly proposed Adaptive Black Widow Optimization (ABWO) algorithm, which makes the proposed de-hazing scheme fully automatic. Qualitative and quantitative performance analyses, and the results are compared with other state-of-the-art methods. The experimental results reveal that the proposed method performs better as compared with others, independent of the haze density, without losing the natural look of the scene. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images
(Nature Research, 2025) Chanchal, A.K.; Lal, S.; Suresh, S.
Kidney cancer is a leading cause of cancer-related mortality, with renal cell carcinoma (RCC) being the most prevalent form, accounting for 80–85% of all renal tumors. Traditional diagnosis of kidney cancer requires manual examination and analysis of histopathology images, which is time-consuming, error-prone, and depends on the pathologist’s expertise. Recently, deep learning algorithms have gained significant attention in histopathology image analysis. In this study, we developed an efficient and robust deep learning architecture called RenalNet for the classification of subtypes of RCC from kidney histopathology images. The RenalNet is designed to capture cross-channel and inter-spatial features at three different scales simultaneously and combine them together. Cross-channel features refer to the relationships and dependencies between different data channels, while inter-spatial features refer to patterns within small spatial regions. The architecture contains a CNN module called multiple channel residual transformation (MCRT), to focus on the most relevant morphological features of RCC by fusing the information from multiple paths. Further, to improve the network’s representation power, a CNN module called Group Convolutional Deep Localization (GCDL) has been introduced, which effectively integrates three different feature descriptors. As a part of this study, we also introduced a novel benchmark dataset for the classification of subtypes of RCC from kidney histopathology images. We obtained digital hematoxylin and eosin (H&E) stained WSIs from The Cancer Genome Atlas (TCGA) and acquired region of interest (ROIs) under the supervision of experienced pathologists resulted in the creation of patches. To demonstrate that the proposed model is generalized and independent of the dataset, it has experimented on three well-known datasets. Compared to the best-performing state-of-the-art model, RenalNet achieves accuracies of 91.67%, 97.14%, and 97.24% on three different datasets. Additionally, the proposed method significantly reduces the number of parameters and FLOPs, demonstrating computationally efficient with 2.71 × FLOPs & 0.2131 × parameters. © The Author(s) 2025.
Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images
(Institute of Electrical and Electronics Engineers Inc., 2025) Chanchal, C.A.; Lal, S.; Suresh, S.
Kidney cancer is a commonly diagnosed cancer disease in recent years, and Renal Cell Carcinoma (RCC) is the most common kidney cancer responsible for 80% to 85% of all renal tumors. The diagnosis of kidney cancer requires manual examination and analysis of histopathological images of the affected tissue. This process is time-consuming, prone to human error, and highly depends on the expertise of a pathologist. Early detection and grading of kidney cancer tissues enable doctors and practitioners to decide the further course of treatment. Therefore, quick and precise analysis of kidney cancer tissue images is extremely important for proper diagnosis. Recently, deep learning algorithms have proved to be very efficient and accurate in histopathology image analysis. In this paper, we propose a computationally efficient deep-learning architecture based on convolutional neural networks (CNNs) to automate the grading and classification task for kidney cancer tissue. The proposed Robust CNN (RoCNN) architecture is capable of learning features at varying convolutional filter sizes because of the inception modules employed in it. Squeeze and Extract (SE) blocks are used to remove unnecessary contributions from noisy channels and improve model accuracy. Concatenating samples from three different parts of architecture allows for the encompassing of varied features, further improving grading and classification accuracy. To demonstrate that the proposed model is generalized and independent of the dataset, it has experimented on two well-known datasets, the KMC kidney dataset of five different grades and the TCGA dataset of four classes. Compared to the best-performing state-of-the-art model the accuracy of RoCNN shows a significant improvement of about 4.22% and 3.01% for both datasets respectively. © 2013 IEEE.
Enhanced JAYA optimization based medical image fusion in adaptive non subsampled shearlet transform domain
(Elsevier B.V., 2022) Suresh, S.; Rajan, M.; Asha, C.S.; Shyam, L.
Multi-modal image fusion has gained popularity in the medical field as it assists doctors to view the diverse medical image modalities in a single image. The treatment is effectively planned by looking into the fused image that helps doctors diagnose diseases. The medical image fusion aims to merge the texture features from multiple images in a single image. The proposed method includes the application of Adaptive window-based Non-Subsampled Shearlet Transform (ANSST) on source images to separate the low and high-frequency directional sub-bands. Further, an enhanced JAYA (EJAYA) optimization framework is utilized to obtain the adaptive weights for combining high-frequency sub-bands for a multi-modal medical image fusion. The low-frequency bands are fused using the max rule based on the average energy of low-frequency sub-bands. The entire process focuses on preserving the low-frequency band's energy while improving the texture details in the combined image. In the end, inverse ANSST is applied on merged low-frequency and high-frequency components to get the fused image. Extensive experiments are conducted on data sets obtained from the Brain Atlas website comprising more than 100 images. The significance of the current approach is validated by qualitative and quantitative assessments. The proposed method exhibits good performance in terms of subjective analysis compared to the recent well-known image fusion techniques. © 2022 Karabuk University
A Framework for Quality Enhancement of Multispectral Remote Sensing Images
(2018) Suresh, S.; Das, D.; Lal, S.
Researches in satellite image enhancement have been particularly confined to two major areas-contrast enhancement and image de noising of remote sensing images. The processing of relatively dark or shadowed images necessitates the need for robust remote sensing enhancement techniques. In this paper, a robust framework for quality enhancement of multispectral remote sensing images is proposed. The quantitative results of proposed algorithm and other existing remote sensing enhancement algorithms are calculated in terms of DE, NIQMC, BIQME, PisDist and CM on different remote sensing and other image databases. Results reveal that visual enhancement of the proposed algorithm is better than other existing remote sensing enhancement algorithms. Finally, the simulation experimental results show that proposed algorithm is effective and efficient for remotes sensing as well as natural images. � 2017 IEEE.
Identification of Voicing Assimilation From Childrenâ€™s Speech
(Institute of Electrical and Electronics Engineers Inc., 2017) Ramteke, P.B.; Madugula, M.; Suresh, S.; Koolagudi, S.G.
In this paper, an attempt has been made for the automatic identification of the voicing assimilation or harmony process. In these processes the voiced sounds are replaced by unvoiced sounds and vice versa. The phonological processes appear in the children represent the age wise speech learning ability, where the processes start to disappear as children grow. Speech Language Pathologists (SLPs) analyse these processes to evaluate the learning ability of the children. The pitch is present in voiced speech and absent in unvoiced region of speech. This gives the clear view of the assimilation; hence pitch is explored for the identification of voicing assimilation. Features extracted from the test words (mispronounced words) are compared with the reference/correct words using Dynamic Time Warping (DTW) and region of mispronunciation is identified from the properties of DTW curve. The highest accuracy of identifying voicing assimilation achieved using pitch feature is 88%. Â© INDIACom-2017.
Image quality restoration framework for contrast enhancement of satellite remote sensing images
(Elsevier B.V., 2018) Suresh, S.; Das, D.; Lal, S.; Gupta, D.
Researches in satellite remote sensing images mainly revolves around enhancement of contrast and removal of noise in image, which affects the data comprehensibility and clarity. Hence, it is always a challenge to process the satellite remote sensing images in order to obtain better quality images with enhanced visibility and minimum image artifacts for improving their application value. In this paper, an effective quality enhancement framework is proposed, which mainly focuses on contrast enhancement of satellite remote sensing images. Several satellite remote sensing images were tested to ratify the effectiveness of the proposed method over other existing remote sensing enhancement methods and their quantitative results are borne out by NIQMC (No Reference Image Quality Metric for Contrast distortion), BIQME (Blind Image Quality Measure of Enhanced images), MICHELSON (Michelson Contrast), DE (Discrete Entropy), EME (Measure of enhancement) and PIXDIST (Pixel distance) along with qualitative results comparison. Results depict that the visual enhancement obtained using the proposed method is superior to other existing enhancement methods. Finally, the simulation results unveil that proposed method is effective and efficient for satellite remotes sensing images. © 2018 Elsevier B.V.
Influence of bulk post processing techniques on anisotropy of microstructural and tribological properties of L-DED produced Ti64 alloy
(Elsevier Ltd, 2025) Suresh, S.; Joshy, J.; Kuriachen, B.; Gurugubelli, R.C.; Kumar, V.; Bontha, S.
Laser-Direct Energy Deposited (L-DED) Ti64 alloy is known to have high anisotropy, and low wear resistance which reduce the longevity of artificial bone joints. Thus, the primary objective of this study is to compare and contrast the effect of bulk treatments to mitigate these inherent limitations. Keeping printing parameters constant, the printed samples were put through different post-treatments, namely, super-? annealing (1050 °C, 1 h) and deep cryogenic dipping (?196 °C, 48 h). Electron back scatter diffraction (EBSD) and x-ray diffraction (XRD) analysis revealed differences in grain morphology and phase distributions in the treated samples. A linear reciprocating wear test is conducted with Al2O3 as the counter body to mimic the artificial hip socket. The super-? annealing process reduced the anisotropy in wear rate from 76 % to 60 % but did not show an overall betterment. On the other hand, the cryo-treatment showed an 83 % reduction in wear and a slight reduction in anisotropy compared to the as-build sample. The coefficient of friction (COF) plots also displayed an increase for annealed samples (15.4%–31.5 % higher) while showing a major reduction in cryo-treated samples (42.8%–54.7 % reduction). © 2025 Elsevier B.V.
Influence of Process Parameters on Microstructural Properties of L-DED Produced Ti64 Alloy
(Springer Science and Business Media Deutschland GmbH, 2025) Suresh, S.; Kuriachen, B.; Kumar, V.; Bontha, S.; Gurugubelli, R.C.
Additive manufacturing (AM) techniques have revolutionized the manufacturing of complex and customized parts across various applications. However, they are known for producing titanium parts with high anisotropy and low ductility, due to high cooling gradient in the build direction and the presence of martensite phase in microstructure respectively. These are inherent problems which limit their application in critical engineering fields. Laserâ€”Direct Energy Deposition (L-DED) produced parts also have the same disadvantages. Thus, the primary objective of this paper is to identify the optimal combination of process parameters for L-DED that can mitigate these inherent limitations. Keeping the parameters such as powder size, orientation angle and hatch angle as constant, the laser power and scan speed are varied to fabricate 9 different sets of samples using L-DED. The research methodology includes an analysis of the microstructure, focusing on grain width, phase distribution, lath characteristics and presence of defects, if any. Microscopy and XRD techniques were used to observe the microstructure. Additionally, hardness studies were performed to evaluate the changes in material hardness. It was noticed thatÂ laser power significantly influences Î² width and Î±â€™ length while scan speed has a lesser dominant effect on both of them. The findings will contribute to the development of process-structure-property relations for L-DED-produced Ti64 and further, optimized manufacturing strategies for producing titanium parts with reduced anisotropy and increased ductility. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Modified differential evolution algorithm for contrast and brightness enhancement of satellite images
(Elsevier Ltd, 2017) Suresh, S.; Lal, S.
Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified Differential Evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no-reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend. © 2017 Elsevier B.V.
Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images
(Elsevier Ltd, 2017) Suresh, S.; Lal, S.
This paper proposes an improved variant of Darwinian Particle Swarm Optimization algorithm based on chaotic functions. Most of the evolutionary algorithms faces the problem of getting trapped in local optima in its search for global optimum solutions. This is highly influenced by the use of random sequences by different operators in these algorithms along their run. The proposed algorithm replaces random sequences by chaotic sequences mitigating the problem of premature convergence. Experiments were conducted to investigate the efficiency of 10 defined chaotic maps and the best one was chosen. Performance of the proposed Chaotic Darwinian Particle Swarm Optimization (CDPSO) algorithm is compared with chaotic variants of optimization algorithms like Cuckoo Search, Harmony Search, Differential Evolution and Particle Swarm Optimization exploiting the chosen optimal chaotic map. Various histogram thresholding measures like minimum cross entropy and Tsallis entropy were used as objective functions and implemented for satellite image segmentation scenario. The experimental results are validated qualitatively and quantitatively by evaluating the mean, standard deviation of the fitness values, PSNR, MSE, SSIM and the total time required for the execution of each optimization algorithm. © 2017 Elsevier B.V.