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Koliwad, Shivaprakash
- Classification of Remote Sensed Data Using Hybrid Method Based on Ant Colony Optimization with Electromagnetic Metaheuristic
Abstract Views :291 |
PDF Views:91
Authors
Affiliations
1 Department of Electronics and Communication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570 016, IN
2 Department of Electronics and Communication Engineering, ATME College of Engineering, Mysuru 570 028, IN
3 PES Institute of Technology and Management, Shivamogga 577 204, IN
4 Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan 573 202, IN
1 Department of Electronics and Communication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570 016, IN
2 Department of Electronics and Communication Engineering, ATME College of Engineering, Mysuru 570 028, IN
3 PES Institute of Technology and Management, Shivamogga 577 204, IN
4 Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan 573 202, IN
Source
Current Science, Vol 113, No 02 (2017), Pagination: 284-291Abstract
In this study, a hybrid configuration of electromagnetic metaheuristic algorithm (EM) with Pachycondyla apicalis (API) ant algorithm (inspired by the behaviour of real ant colony Pachycondyla apicalis) belonging to ant colony optimization (ACO) called EMAPI algorithm is presented for remote sensing data classification. The traditional per-pixel classification method identifies the classes using spectral variance and ignores the spatial distribution of pixels. It requires training data to be normally distributed in the pixels corresponding to land use/land cover classes and creates a lot of confusion between classes within a remote sensed (RS) data. The proposed algorithm is an integrated strategy structure to achieve advantages of global and local search ability of EM and API algorithms respectively. The objective consists of improving overall accuracy of the classified results of RS data. This method can overcome intermixing with regard to scrub land with cultivated areas and build-up land with palm groves. The proposed algorithm is tested on objective functions well used in the literature and EMAPI is used for supervised land cover classification. Results of EMAPI algorithm over 6 classes showed an improvement of 8% in overall classification accuracy (OCA) for EM technique and improvement of 3% in OCA for API algorithm.Keywords
Ant Colony Optimization, API Algorithm, Electromagnetic Metaheuristic, Data Classification, Hybrid Metaheuristic.Full Text
References
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- Fusion of Multispectral and Panchromatic Data using Regionally Weighted Principal Component Analysis and Wavelet
Abstract Views :218 |
PDF Views:74
Authors
Affiliations
1 Department of Electronics and Communication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570 016,, IN
2 SDM Institute of Technology, Ujire, Belthangady 574 240, IN
3 Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan 573 202, IN
1 Department of Electronics and Communication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570 016,, IN
2 SDM Institute of Technology, Ujire, Belthangady 574 240, IN
3 Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan 573 202, IN
Source
Current Science, Vol 115, No 10 (2018), Pagination: 1938-1942Abstract
This study proposes a new multispectral (MS) and panchromatic (PAN) image fusion algorithm based on regionally weighted principal component analysis (RW-PCA) and wavelet. First, the MS images are segmented into spectrally similar regions based on the fuzzy c-means (FCM) clustering method. Secondly, based on the spectral vector’s degree of membership in each region, a new RW-PCA method is proposed to fuse the MS and PAN images region by region, and fused MS images are obtained. In the traditional PCA-based fusion method, the MS and PAN images are fused globally with the same transform method. In the proposed RW-PCA-based fusion method, the local spectrum information of the MS images is employed, and the spectral information is better preserved in the fused MS images. Finally, in order to improve the quality of spectral and spatial details, the above fused MS images and the original PAN images are further fused using the wavelet-based fusion method, and the final fused MS images are obtained. Experimental results demonstrated that the proposed image fusion algorithm performs better in spectral preservation and spatial quality improvement than some other methods do.Keywords
Fuzzy, RWPCA_WT, Regionally Weighted, WT.Full Text
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