Extended Biogeography Based Optimization for Natural Terrain Feature Classification from Satellite Remote Sensing Images

Gupta, Sonakshi; Arora, Anuja; Panchal, V. K.; Goel, Shubhra

doi:10.1007/978-3-642-22606-9_28

Cited by 17 publications

(8 citation statements)

References 4 publications

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“…The variables that characterize habitability are called suitability index variables (SIVs). BBO has already been hybridized with various other techniques for the purpose of classification [6,[11][12][13]19]. Here in this paper, we have proposed a hybrid technique of this algorithm with Cuckoo Search and found satisfactory results.…”

Section: Biogeography Based Optimization (Bbo)mentioning

confidence: 93%

Novel approaches for classification based on Cuckoo Search Strategy

Goel

Sharma

Bedi

2013

HIS

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This paper introduces a novel bio inspired clustering algorithm called Cuckoo Search Clustering Algorithm (CSCA). This algorithm is based on the recently proposed Cuckoo Search Optimization technique which mimics the breeding strategy of the parasitic bird-cuckoo. The algorithm is further extended to a classification method, Biogeography Based Cuckoo Search Classification Algorithm (BCSCA), which is a hybrid approach of the two nature inspired metaheuristic techniques. The proposed algorithms are validated with real time remote sensing satellite image datasets. The CSCA was first tested with benchmark dataset, which yields good results. Inspired by the results, it was applied on two real time remote sensing satellite image datasets for extraction of the water body, which itself is a quite complex problem. A new method for the generation of new cuckoos has been proposed, which is used in the algorithms. The resulting algorithm is conceptually simpler, takes less parameter than other nature inspired algorithms, and, after some parameter tuning, yields very good results. The extended algorithm BCSCA is also tested on the same satellite image for identifying different land covers by classifying the image in various classes. The algorithm was successful in classifying other land cover regions like rocky, barren, urban and vegetation. We strongly feel that results can be further improved by finer tuning of the parameters. Both the algorithms use Davies-Bouldin index (DBI) as fitness function. Further exploration of suggested algorithms CSCA and BCSCA may prove them to be strong entrants in the pool of nature inspired techniques.

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Section: Biogeography Based Optimization (Bbo)mentioning

confidence: 93%

Novel approaches for classification based on Cuckoo Search Strategy

Goel

Sharma

Bedi

2013

HIS

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“…Johal et al [10] have presented the theory of Flower Pollination by Artificial Bees with the BBO for the satellite images. In this technique, the FPAB do the groups of the pixels of the satellite image.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid ACO based Optimized RVM Algorithm for Land Cover Satellite Image Classification

Badusha¹,

Mohideen²

2018

EAI Endorsed Transactions on Energy Web

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“…Markov models are also derived for BBO with selection, migration, and mutation operators [30], which can provide theoretically exact limiting probabilities for each possible population distribution for a given problem. BBO has shown good performance, both on benchmark problems [28] and on real-world problems (e.g., aircraft engine sensor selection [28], economic load dispatch [31], and satellite image classification [32]). …”

Section: Nomenclaturementioning

confidence: 99%

Biologically Inspired Model with Feature Selection for Target Recognition Using Biogeography-Based Optimization

Duan

Deng

2014

Journal of Aerospace Information Systems

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To detect salient ground targets precisely and rapidly during aerial reconnaissance, this paper describes a novel object recognition method based on the feature selection of a biologically inspired model and biogeography-based optimization. As a promising approach to object recognition, the biologically inspired model is a hierarchical system of building an increasingly complex and invariant feature representation, which closely follows the process of object recognition in the visual cortex. These scale-and position-tolerant features are constructed by alternating between a template-matching and a maximum-pooling operation. Because of the many patches extracted in the standard biologically inspired model, the random mechanism may extract patches from irrelevant parts of an image and consume a lot of time. In this work, a feature selection method is proposed based on a new population-based evolutionary algorithm called biogeography-based optimization to choose the proper set of patches with high accuracy of classification and recognition. A support vector machine classifier is used for evaluation of the fitness function in biogeography-based optimization and to calculate the recognition rate in testing. A series of experiments are conducted, and the comparative results demonstrate the feasibility and effectiveness of the approach. = size of the patch P i P i = one patch extracted at the C1 layer P s = probability of the habitat containing exact species S k = number of species w a = weight associated to the error rate w f = weight associated to the normalized summation of selected patches x = horizontal coordinate y = vertical ordinate β = sharpness of the exponential function γ = aspect ratio of Gabor filter θ = orientation of Gabor filter λ = wavelength of Gabor filter λ k = immigration rate of the kth solution μ k = emigration rate of the kth solution σ = effective width of Gabor filter

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Extended Biogeography Based Optimization for Natural Terrain Feature Classification from Satellite Remote Sensing Images

Cited by 17 publications

References 4 publications

Novel approaches for classification based on Cuckoo Search Strategy

Novel approaches for classification based on Cuckoo Search Strategy

A Hybrid ACO based Optimized RVM Algorithm for Land Cover Satellite Image Classification

Biologically Inspired Model with Feature Selection for Target Recognition Using Biogeography-Based Optimization

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