Satellite Image Classification with Data Augmentation and Convolutional Neural Network

机译：具有数据增强和卷积神经网络的卫星图像分类

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Satellite image classification is helpful in many real-time applications for better utilization of area and to get deep information from it. It is difficult to classify them as they are having high inter-class overlapping features. In this paper, a novel approach to classify satellite images is developed based on convolutional neural network (CNN). The model is trained on the basis of data (image) augmentation with different parameters. Using filters, CNN model learns spatial information of given RGB image and creates a robust system for classification. The results are tested on benchmark PatternNet [1] dataset with different image augmentation parameters and size of it. Significant amount of accuracy is achieved using the proposed technique.

机译：卫星图像分类在许多实时应用中有助于更好地利用区域并从中获取深度信息。难以将它们分类，因为它们具有高级间重叠功能。本文基于卷积神经网络（CNN）开发了一种对卫星图像进行分类的新方法。使用不同参数的数据（图像）增强模型进行培训。使用过滤器，CNN模型了解给定RGB图像的空间信息，并为分类创建强大的系统。结果在基准图案网络上进行测试，具有不同的图像增强参数和其大小的数据集。使用所提出的技术实现了大量的精度。

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《International Conference in Advances in Electrical and Computer Technologies》|2019年|83-92|共10页
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Parth R. Dave; Hariom A. Pandya;
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Convolution neural network (cnn); Image augmentation; Average pooling; Max pooling; Callback;

机译：卷积神经网络（CNN）;图像增强;平均池;最大汇集;打回来;

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Satellite Image Classification with Data Augmentation and Convolutional Neural Network

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