T. Vignesh ¹, K. K. Thyagharajan ², D. Murugan ³, M. Sakthivel ⁴, S. Pushparaj ⁴

Affiliations
1 Department of Computer Science and Engineering, S. A. Engineering College, Chennai - 600077, Tamil Nadu, IN
2 R. M. D Engineering College, Kavaraipettai - 601206, IN
3 Department of Computer Science and Engineering, M. S. University, Tirunelveli - 627012,Tamil Nadu, IN
4 Department of Geography, University of Madras, Chennai - 600005, Tamil Nadu, IN

Abstract

Objectives: To classify the satellite images into different land use/land cover classes such as water, building, cropland, forest, etc, to monitor the environmental impacts. Method: In this paper, images are grouped into various clusters using a novel SVD trace function clustering algorithm. The clustered samples are used as a training set in a novel unsupervised Ensemble Minimization Learning algorithm (EML) for classification. The main aim of using EML is to classify the forest, vegetative land patterns, build up area in rural and urban areas with the use of best accuracy rate. Finding: Our proposed methods provides 90.56% classification rate with low error rate. This EML applies multinomial probit model and ensembles simulated data set and improves the learning of nonlinear relationships between the classified attributes. Multinomial probit model is used to bring all the related possible segmented values to fall into one single category, thus increasing the classification accuracy. Our proposed methods experimented with three different real data sets. The experimental results indicate that our proposed unsupervised model outperforms than the previous techniques. Application: It could be using for land use/land cover change detection, under water object identification, coastal area monitoring, etc. Improvement: In future it could be apply in video data and could be improve the classification accuracy also.

Keywords

Ensemble Minimization Learning algorithm, Land use/Land Cover Classification, Multinominal Probit Model, SVD Trace Function, Unsupervised Algorithm.

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M. Sakthivel ¹, D. Dhayalan ¹, M. Saleem Babu ², J. Senthil Murugan ¹

Affiliations
1 Department of MCA, Vel Tech High Tech Engineering College, Chennai - 600062, Tamil Nadu, IN
2 Department of CSE, Jaya Engineering College, Chennai - 602024, Tamil Nadu, IN

Abstract

Background: Aircraft Recognition with high resolution space borne images is a challenging task.It is different from other natural object recognition. The ground image of aircraft is complex and disturbing. The target image has to be detected in a cluttered background. Methods: The proposed technique takes into account multiple features such as circular Frequency filter (CF-filter), fuselage symmetry and angle between fuselage axis and wing for candidate selection. Normalized Tree cut algorithm isused for Segmentation of image done and aircraft alignment done using Histogram Oriented Gradient (HOG). Automatic jigsaw puzzle solution is implemented for aircraft reconstruction of pieces. Findings: In the candidate selection stage because of cluttered background the aircraft is not identified. Here the multiple identification techniques such ascircular frequency filter (CF-filter), fuselage symmetry and angle between fuselage axis and wing are considered. Applications: The above said techniques were dealt earlier as independent features. Because of proposed multiple feature consideration accuracy in candidate identification is enhanced.

Keywords

Aircraft Recognition, Jigsaw Puzzle Algorithm, Histogram Oriented Gradient

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