Indian River Watershed Image Analysis Using Fuzzy-CA Hybrid Approach

Image segmentation among overlapping land cover areas in satellite images is a very crucial task. Detection of belongingness is the important problem for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of Fuzzy C-Means and Cellular automata methods. This new unsupervised method is able to detect clusters using 2-Dimensional Cellular Automata model based on fuzzy segmentations. This approach detects the overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase of our method, we utilize a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. We experiment our method on Indian Ajoy river watershed area. The clustered regions are compared with well-known FCM and K-Means methods and also with the ground truth knowledge. The results show the superiority of our new method.

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Landcover Change Detection Using PSO-Evaluated Quantum CA Approach on Multi-Temporal Remote-Sensing Watershed Images

Quantum-Inspired Intelligent Systems for Multimedia Data Analysis - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5219-2.ch006 ◽

2018 ◽

pp. 178-212

Author(s):

Kalyan Mahata ◽

Rajib Das ◽

Subhasish Das ◽

Anasua Sarkar

Keyword(s):

Cellular Automata ◽

Discrete Dynamical System ◽

Computational Cost ◽

Ground Truth ◽

Rule Base ◽

Quantum Cellular Automata ◽

Landcover Change ◽

Novel Approach ◽

Mixed Pixels ◽

Multi Temporal

Computer science plays a major role in image segmentation and image processing applications. Despite the computational cost, PSO evaluated QCA approaches perform comparable to or better than their crisp counterparts. This novel approach, proposed in this chapter, has been found to enhance the functionality of the CA rule base and thus enhance the established potentiality of the fuzzy-based segmentation domain with the help of quantum cellular automata. This new unsupervised method is able to detect clusters using 2-dimensional quantum cellular automata model based on PSO evaluation. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase, it utilizes a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. The authors experiment on Tilaya Reservoir Catchment on Barakar River. The clustered regions are compared with well-known PSO, FCM, and k-means methods and also with the ground truth knowledge. The results show the superiority of the new method.

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Landcover Change Detection Using PSO-Evaluated Quantum CA Approach on Multi-Temporal Remote-Sensing Watershed Images

Environmental Information Systems ◽

10.4018/978-1-5225-7033-2.ch029 ◽

2019 ◽

pp. 679-705

Author(s):

Kalyan Mahata ◽

Rajib Das ◽

Subhasish Das ◽

Anasua Sarkar

Keyword(s):

Cellular Automata ◽

Discrete Dynamical System ◽

Computational Cost ◽

Ground Truth ◽

Rule Base ◽

Quantum Cellular Automata ◽

Landcover Change ◽

Novel Approach ◽

Mixed Pixels ◽

Multi Temporal

Computer science plays a major role in image segmentation and image processing applications. Despite the computational cost, PSO evaluated QCA approaches perform comparable to or better than their crisp counterparts. This novel approach, proposed in this chapter, has been found to enhance the functionality of the CA rule base and thus enhance the established potentiality of the fuzzy-based segmentation domain with the help of quantum cellular automata. This new unsupervised method is able to detect clusters using 2-dimensional quantum cellular automata model based on PSO evaluation. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase, it utilizes a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. The authors experiment on Tilaya Reservoir Catchment on Barakar River. The clustered regions are compared with well-known PSO, FCM, and k-means methods and also with the ground truth knowledge. The results show the superiority of the new method.

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Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

Journal of Intelligent Systems ◽

10.1515/jisys-2019-0155 ◽

2020 ◽

Vol 30 (1) ◽

pp. 273-286

Author(s):

Kalyan Mahata ◽

Rajib Das ◽

Subhasish Das ◽

Anasua Sarkar

Keyword(s):

Land Use ◽

Image Segmentation ◽

Cellular Automata ◽

Land Cover ◽

Satellite Imagery ◽

Current Model ◽

Discrete Dynamical System ◽

Land Use Land Cover ◽

Data Set ◽

Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

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Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach

Advances in Computational Intelligence and Robotics - Handbook of Research on Swarm Intelligence in Engineering ◽

10.4018/978-1-4666-8291-7.ch014 ◽

2015 ◽

pp. 435-468

Author(s):

Anasua Sarkar ◽

Rajib Das

Keyword(s):

Remote Sensing ◽

Particle Swarm Optimization ◽

Optimization Technique ◽

Hybrid Approach ◽

Particle Swarm ◽

Ground Truth ◽

Optimization Methods ◽

Pixel Classification ◽

Swarm Optimization ◽

Mixed Pixels

Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on fuzzy membership values. This approach addresses overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. PSO is a population-based stochastic optimization technique inspired from the social behavior of bird flocks. The authors demonstrate the algorithm for segmenting a LANDSAT image of Shanghai. The newly developed algorithm is compared with FCM and K-Means algorithms. The new algorithm-generated clustered regions are verified with the available ground truth knowledge. The validity and statistical analysis are performed to demonstrate the superior performance of the new algorithm with K-Means and FCM algorithms.

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Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach

Geospatial Intelligence ◽

10.4018/978-1-5225-8054-6.ch029 ◽

2019 ◽

pp. 636-669

Author(s):

Anasua Sarkar ◽

Rajib Das

Keyword(s):

Remote Sensing ◽

Particle Swarm Optimization ◽

Optimization Technique ◽

Hybrid Approach ◽

Particle Swarm ◽

Ground Truth ◽

Optimization Methods ◽

Pixel Classification ◽

Swarm Optimization ◽

Mixed Pixels

Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on fuzzy membership values. This approach addresses overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. PSO is a population-based stochastic optimization technique inspired from the social behavior of bird flocks. The authors demonstrate the algorithm for segmenting a LANDSAT image of Shanghai. The newly developed algorithm is compared with FCM and K-Means algorithms. The new algorithm-generated clustered regions are verified with the available ground truth knowledge. The validity and statistical analysis are performed to demonstrate the superior performance of the new algorithm with K-Means and FCM algorithms.

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Comparative Assessment of the Built-Up Area Expansion Based on Corine Land Cover and Landsat Datasets: A Case Study of a Post-Socialist City

Remote Sensing ◽

10.3390/rs12132137 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2137 ◽

Cited By ~ 1

Author(s):

Ilinca-Valentina Stoica ◽

Marina Vîrghileanu ◽

Daniela Zamfir ◽

Bogdan-Andrei Mihai ◽

Ionuț Săvulescu

Keyword(s):

Land Cover ◽

Urban Expansion ◽

Ground Truth ◽

Cost Effective ◽

Planning System ◽

The European Union ◽

Scale Level ◽

Corine Land Cover ◽

Macro Scale ◽

Area Expansion

Monitoring uncontained built-up area expansion remains a complex challenge for the development and implementation of a sustainable planning system. In this regard, proper planning requires accurate monitoring tools and up-to-date information on rapid territorial transformations. The purpose of the study was to assess built-up area expansion, comparing two freely available and widely used datasets, respectively, Corine Land Cover and Landsat, to each other, as well as the ground truth, with the goal of identifying the most cost-effective and reliable tool. The analysis was based on the largest post-socialist city in the European Union, the capital of Romania, Bucharest, and its neighboring Ilfov County, from 1990 to 2018. This study generally represents a new approach to measuring the process of urban expansion, offering insights about the strengths and limitations of the two datasets through a multi-level territorial perspective. The results point out discrepancies between the datasets, both at the macro-scale level and at the administrative unit’s level. On the macro-scale level, despite the noticeable differences, the two datasets revealed the spatiotemporal magnitude of the expansion of the built-up area and can be a useful tool for supporting the decision-making process. On the smaller territorial scale, detailed comparative analyses through five case-studies were conducted, indicating that, if used alone, limitations on the information that can be derived from the datasets would lead to inaccuracies, thus significantly limiting their potential to be used in the development of enforceable regulation in urban planning.

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