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Comparison of computational intelligence based classification techniques for remotely sensed optical image classification

机译：基于计算智能的遥感光学图像分类技术比较

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摘要

Several computational intelligence components, namely neural networks (NNs), fuzzy sets, and genetic algorithms (GAs), have been applied separately or in combination to the process of remotely sensed data classification. By applying computational intelligence, we expect increased accuracy through the use of NNs, optimal NN structure and parameter determination via GAs, and transparency using fuzzy sets is expected. This paper systematically reviews and compares several configurations in the particular context of remote sensing for land cover. In addition, some of the configurations used here, such as NEFCASS and CANFIS, have few previous applications in the field. A comparison of the configurations is achieved by testing the different methods with exactly the same case-study data. A thorough assessment of results is performed by constructing an accuracy matrix for each training and testing data set. The evaluation of different methods is not only based on accuracy but also on compactness, completeness, and consistency. The architecture, produced rule set, and training parameters for the specific classification task are presented. Some comments and directions for future work are given.
机译：几种计算智能组件，即神经网络（NN），模糊集和遗传算法（GA），已单独或组合应用于遥感数据分类过程。通过应用计算智能，我们期望通过使用神经网络，通过遗传算法实现最佳的神经网络结构和参数确定来提高准确性，并期望使用模糊集来提高透明度。本文系统地回顾和比较了土地覆盖遥感在特定情况下的几种配置。另外，此处使用的某些配置（例如NEFCASS和CANFIS）在该领域中很少有以前的应用。通过使用完全相同的案例研究数据测试不同的方法，可以对配置进行比较。通过为每个训练和测试数据集构建一个精度矩阵，可以对结果进行全面评估。对不同方法的评估不仅基于准确性，而且还基于紧凑性，完整性和一致性。给出了用于特定分类任务的体系结构，产生的规则集和训练参数。给出了一些意见和未来工作的方向。

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