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首页> 外文会议>International Conference on Advanced Technologies for Signal and Image Processing >A symbiotic organisms search algorithm for feature selection in satellite image classification
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A symbiotic organisms search algorithm for feature selection in satellite image classification

机译：一种共生生物搜索算法，用于卫星图像分类中的特征选择

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摘要

The image classification performance depends a lot on the best choice of the descriptors and the techniques used to extract them. With the exponential growth of data in the field of remote sensing, classifying these massive images still remains an open and challenging issue. The high dimensionality of the feature space, not only increases the time and space complexities, but also may reduce the image classification performance in terms of accuracy and time to build the classification model. To overcome this challenge, this paper presents a novel feature selection method based on a combinatorial optimization algorithm for training a feed-forward Artificial Neural Networks to select a small number of features while maintaining good classification rates. The performance of the proposed method is tested on real image dataset and compared with other state-of-the-art methods. The experimental results show that the proposed method has good performances.
机译：图像分类的性能在很大程度上取决于对描述符的最佳选择以及用于提取描述符的技术。随着遥感领域数据的指数增长，对这些海量图像进行分类仍然是一个开放且具有挑战性的问题。特征空间的高维性，不仅增加了时间和空间的复杂性，而且在建立分类模型的准确性和时间方面可能会降低图像分类的性能。为了克服这一挑战，本文提出了一种基于组合优化算法的新颖特征选择方法，用于训练前馈人工神经网络以选择少量特征，同时保持良好的分类率。该方法的性能在真实图像数据集上进行了测试，并与其他最新方法进行了比较。实验结果表明，该方法具有良好的性能。

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