Remote sensing-ieee-2014-projects
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The document discusses advancements in change detection of hyperspectral remote sensing images through multilevel image segmentation using a new method based on fractional-order Darwinian particle swarm optimization (FODPSO). It highlights the difficulties of accurate segmentation due to the high dimensionality of hyperspectral data and offers an optimized solution that improves classification accuracy while reducing computational time. Experimental results demonstrate that FODPSO outperforms other bioinspired methods, yielding favorable outcomes in segmentation and classification tasks.
Remote sensing-ieee-2014-projects
- 1. Remote Sensing IEEE 2014 Projects Web : www.kasanpro.com Email : sales@kasanpro.com List Link : http://kasanpro.com/projects-list/remote-sensing-ieee-2014-projects Title :Change Detection of Hyper Spectral Remote Sensing Image by Multilevel Image Segmentation Language : Java Project Link : http://kasanpro.com/p/java/change-detection-hyper-spectral-remote-sensing-image-multilevel-image-segmentation Abstract : Land cover composition and change are important factors that affect ecosystem condition and function. Remote sensing is the most important and effective way to acquire data of land cover. The paper proposes an Improved Change detection with Hyperspectral remote sensing images. Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the seg- mentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. And finally Post-classification Comparison Change Detection applied which is the most commonly used quantitative method of change detection. Title :Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization Language : Java Project Link : http://kasanpro.com/p/java/multilevel-image-segmentation-based-particle-swarm-optimization Abstract : Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.