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satellite image segmentation by Using Grey Wolf Optimizer with Masi Entropy

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2025年 1-21页

作者： Bandikolla, Lakshmi Khairuzzaman, Abdul Kayom Md VIT AP Univ Sch Elect Engn Vijayawada 522237 Andhra Pradesh India

image segmentation is an essential technique in computer vision and image processing, offering a broad spectrum of applications, such as satellite and aerial imagery, medical imaging, object detection, agriculture, industrial inspection, document processing and traffic monitoring. satellite image segmentation is used to detect various objects such as water bodies, roads, buildings and disaster-affected areas. This work proposes a new method for multilevel thresholding based satellite image segmentation using Masi entropy as an objective function. The threshold searching process is optimized by the grey wolf optimizer. The quality of segmented images is assessed using widely recognized evaluation metrics, including peak signal to noise ratio, mean structural similarity, feature similarity. Additionally, the entropic value is measured using Masi entropy, while computational complexity is assessed based on CPU run time. Together, these metrics efficiently offer a thorough assessment of both the segmentation quality and the algorithm's performance. The proposed method is compared with several existing methods, such as the Genetic Algorithm with Kapur's entropy, the Multilevel Thresholding-moth Flame Optimization with the Otsu method, Chaotic Darwinian Particle Swarm Optimization with the Otsu method and the Dynamic Harris Hawks with the mutation using Kapur's entropy. The comparative study shows that the proposed method can be efficiently applied in satellite image segmentation and higher level image processing and computer vision applications that require segmentation.

关键词： Grey wolf optimizer Metaheuristic optimization techniques Masi entropy Multilevel thresholding satellite image segmentation

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TripleGAN: Multi-Adversarial Network Domain Adaptation for satellite image segmentation 35

TripleGAN: Multi-Adversarial Network Domain Adaptation for S...

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35th Chinese Control and Decision Conference (CCDC)

作者： Wang, Yifei Wang, Siyi Xu, Meng Chen, Kai Xiong, Zhihua Wang, Huangang Tsinghua Univ Dept Automat Beijing Peoples R China Guizhou Acad Sci Guiyang Peoples R China

ISBN: (纸本)9798350334722

satellite image segmentation has become an important auxiliary tool for decision-making in various fields, including city planning, building extraction, traffic control, and environmental protection. However, for satellite images, there is a significant challenge that the datasets are often small and lacked of label. To address this issue, we propose a novel framework called the Triple Generative Adaptive Networks (TripleGAN), which employs domain adaptation to transfer models from labeled datasets to unlabeled ones. TripleGAN is a deep neural network consisting of one segmentation module and three discriminators to reduce the domain gap between labeled and unlabeled datasets. By jointly training three GANs, our model achieves a significantly better performance in handling different data distributions. In experiments on IS-PRS public datasets, TripleGAN outperforms the state-of-the-art model, demonstrating its advantages and providing a promising multi adversarial learning scheme for future researches.

关键词： Generative Adversarial Network satellite image segmentation Domain Adaptation

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TripleGAN:Multi-Adversarial Network Domain Adaptation for satellite image segmentation

TripleGAN:Multi-Adversarial Network Domain Adaptation for Sa...

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第35届中国控制与决策会议

作者： Yifei Wang Siyi Wang Meng Xu Kai Chen Zhihua Xiong Huangang Wang Dept.of Automation Tsinghua University Guizhou Academy of Sciences

satellite image segmentation has become an important auxiliary tool for decision-making in various fields,including city planning,building extraction,traffic control,and environmental ***,for satellite images,there is a significant challenge that the datasets are often small and lacked of *** address this issue,we propose a novel framework called the Triple Generative Adaptive Networks(TripleGAN),which employs domain adaptation to transfer models from labeled datasets to unlabeled *** is a deep neural network consisting of one segmentation module and three discriminators to reduce the domain gap between labeled and unlabeled *** jointly training three GANs,our model achieves a significantly better performance in handling different data *** experiments on ISPRS public datasets,TripleGAN outperforms the state-of-the-art model,demonstrating its advantages and providing a promising multi adversarial learning scheme for future researches.

关键词： Generative Adversarial Network satellite image segmentation Domain Adaptation

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Reference data preparation for complex satellite image segmentation

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IET image PROCESSING 2020年第4期14卷 628-637页

作者： Sharma, Ashu Ghosh, Jayanta Kumar Kolay, Saptarshi Indian Inst Technol Civil Engn Dept Roorkee 247667 Uttar Pradesh India Indian Inst Technol Dept Architecture & Planning Roorkee 247667 Uttar Pradesh India

The high spatial resolution satellite images covering multiple objects of the urban area are visually complex in nature. This visual complexity causes ambiguity during segmentation of such images when the targets are unknown. In such case, reference data are required to assess the segmentation methods. Due to excellence of humans in visual analysis, the presented work has attempted for a psycho-visual approach to prepare the reference segmented complex HRS images. The reference data have has prepared through the correlation of the quantified eye-tracking data (metrics) and corresponding concurrent think-aloud (CTA) data for each of the created segments. Segments get updated based on Gestalt principles and Gibson's theory. Those segments having the best correlation between metrics and CTA data have been opted as the final output. The results suggest that the functional grouping of objects while preferring perceptual grouping for segments drawing conforms the most to the participants' verbal response. The final results have been compared with the existing notion of full segmentation used for complex images. The comparison also proffers the superiority of the proposed segmentation over full segmentation to be used as reference. In the future, a large number of images may be used to prepare better reference data.

关键词： image segmentation data preparation HRS images eye-tracking data CTA data satellite image segmentation high-resolution satellite images segmentation methods visual analysis Gestalt principles Gibson theory psycho-visual-based approach concurrent think-aloud data

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Learning and Adapting Robust Features for satellite image segmentation on Heterogeneous Data Sets

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第9期57卷 6517-6529页

作者： Ghassemi, Sina Fiandrotti, Attilio Francini, Gianluca Magli, Enrico Polytech Univ Turin Elect & Telecommun Dept I-10129 Turin Italy Univ Paris Saclay Telecom Paristech Image Data Signals Dept Multimedia Grp St Aubin France Politecn Torino Telecom Italia I-10148 Turin Italy

This paper addresses the problem of training a deep neural network for satellite image segmentation so that it can be deployed over images whose statistics differ from those used for training. For example, in postdisaster damage assessment, the tight time constraints make it impractical to train a network from scratch for each image to be segmented. We propose a convolutional encoder- decoder network able to learn visual representations of increasing semantic level as its depth increases, allowing it to generalize over a wider range of satellite images. Then, we propose two additional methods to improve the network performance over each specific image to be segmented. First, we observe that updating the batch normalization layers' statistics over the target image improves the network performance without human intervention. Second, we show that refining a trained network over a few samples of the image boosts the network performance with minimal human intervention. We evaluate our architecture over three data sets of satellite images, showing the state-of-the-art performance in binary segmentation of previously unseen images and competitive performance with respect to more complex techniques in a multiclass segmentation task.

关键词： Convolutional neural network (CNN) deep learning domain adaptation encoder-decoder architecture satellite image segmentation

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Fuzzy Multi-level Color satellite image segmentation Using Nature-Inspired Optimizers: A Comparative Study

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2019年第8期47卷 1391-1415页

作者： Dhal, Krishna Gopal Ray, Swarnajit Das, Arunita Galvez, Jorge Das, Sanjoy Midnapore Coll Autonomous Dept Comp Sci & Applicat Paschim Medinipur W Bengal India Skybound Digital LLC Kolkata W Bengal India Kalyani Govt Engn Coll Dept Informat Technol Kalyani Nadia India Univ Guadalajara Dept Elect CUCEI Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico Univ Kalyani Dept Engn & Technol Studies Kalyani Nadia India

In the realm of image processing domain, segmentation is an indispensable method for various applications. One can segment an image according to shape, size, regularities, structure, color, etc. Multi-level thresholding for image segmentation is one of the most promising methods for segmentation in the recent era. However, multi-level thresholding is computationally expensive, tedious and also challenging because of finding the optimal threshold values. Thus, to address this issue, this study presents a stochastic fractal search (SFS) with fuzzy entropy-based multi-level thresholding model for the proper segmentation of color satellite images. To prove the superiority of SFS algorithm, a comparative study is performed with four well-known nature-inspired optimization algorithms, namely particle swarm optimization (PSO), cuckoo search (CS), harmony search (HS) and artificial bee colony (ABC) algorithms. The experiment has been conducted on various satellite images, and the result shows that SFS with fuzzy entropy-based model provides superior-quality segmented images over other methods in terms of fitness value, computational time and values of quality metrics. The experimental study also shows that computational time of SFS algorithms is 2.5% less than CS algorithms and 8%, 9%, 15% less than ABC, PSO, HS, respectively, on average when the same number function evaluations has been considered as stopping criterion.

关键词： satellite image segmentation Multi-level thresholding Fuzzy entropy Swarm intelligence Optimization

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Multi-Strategy Emperor Penguin Optimizer for RGB Histogram-Based Color satellite image segmentation Using Masi Entropy

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IEEE ACCESS 2019年 7卷 134448-134474页

作者： Jia, Heming Sun, Kangjian Song, Wenlong Peng, Xiaoxu Lang, Chunbo Li, Yao Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China

In order to realize the multilevel thresholding segmentation of color satellite images, a multi-strategy emperor penguin optimizer (called MSEPO) is proposed to find the optimal threshold values for three channels of RGB images. Masi entropy is utilized as the objective function. Meanwhile, three strategies are introduced, namely highly disruptive polynomial mutation, Levy flight, and thermal exchange operator. Through these, the MSEPO is able to properly balance the exploration and exploitation mechanisms. Moreover, the convergence, accuracy and stability performance have been significantly enhanced. Tests are carried out on color Berkeley images and color satellite images at various threshold levels. The experimental results show that the proposed method achieves higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), higher Feature Similarity Index (FSIM), and shorter CPU time than seven state-of-the-art optimization techniques. To present in a comprehensive manner, the computational complexity has also been analyzed in terms of time and space complexity. Wilcoxon rank sum test and Friedman test are also applied to statistical analysis. To sum up, MSEPO algorithm has achieved significant improvement and superior performance. Whats more, the proposed technique is more suitable for high-dimensional segmentation of complex satellite images.

关键词： Entropy image segmentation satellites Histograms image color analysis Thresholding (Imaging) Linear programming Multilevel thresholding satellite image segmentation Masi entropy emperor penguin optimizer thermal exchange operator multi-strategy

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A Grey Wolf Optimizer Based Automatic Clustering Algorithm for satellite image segmentation 7

A Grey Wolf Optimizer Based Automatic Clustering Algorithm f...

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7th International Conference on Advances in Computing and Communications (ICACC)

作者： Kapoor, Shubham Zeya, Irshad Singhal, Chirag Nanda, Satyasai Jagannath Malaviya Natl Inst Technol Dept Elect & Commun Engg Jaipur 302017 Rajasthan India

Grey Wolf Optimizer has recently emerged as an efficient meta-heuristic optimization technique. It has good capability in exploitation for unimodal problems, superior exploration ability for multimodal problems, and also works fine for composite functions avoiding local minimas. This paper proposes an application of Grey Wolf Optimizer (GWO) algorithm for satellite image segmentation. The original GWO has been suitably modified to work as an automatic clustering algorithm. This algorithm has been applied on two satellite images. It is computationally efficient and its accuracy is superior in many cases in terms of Davies-Bouldin (DB) index, average inter-cluster distance and average intra-cluster distance. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 7th International Conference on Advances in Computing & Communications.

关键词： Automatic Clustering satellite image segmentation Grey Wolf Optimizer Davies-Bouldin index

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A Grey Wolf Optimizer Based Automatic Clustering Algorithm for satellite image segmentation

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Procedia Computer Science 2017年 115卷 415-422页

作者： Shubham Kapoor Irshad Zeya Chirag Singhal Satyasai Jagannath Nanda Department of Electronics and Communication Engg. Malaviya National Institute of Technology Jaipur-302017 Rajasthan India

关键词： Automatic Clustering satellite image segmentation Grey Wolf Optimizer Davies-Bouldin index

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Different Techniques for satellite image segmentation

Different Techniques for Satellite Image Segmentation

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Proceedings of Online International Conference on Green Engineering and Technologies (IC-GET)

作者： Deepika, N. P. Vishnu, K. Cochin Coll Engn & Technol Dept Comp Sci & Engn Malappuram Kerala India

ISBN: (纸本)9781467397810

image segmentation is one of the emerging trends in the field of image processing. It has found applications in the field of medical applications, satellite imagery etc. image segmentation helps in segmenting the images into sub regions which are of our interest which can be analyzed individually. satellite imagery is one of the wide areas in segmentation. This paper proposes three techniques such as K-means Clustering, Thresholding Technique and Active Contours for satellite image segmentation and evaluate the best method in satellite image segmentation using various performance parameters like segmentation Accuracy, Correlation Ratio.

关键词： satellite image segmentation Active Contours K-means Clustering Threshold segmentation Accuracy Correlation Ratio

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