论文标题
通过向CNN模型注入传统功能，在卫星图像中的土地分类
Land Classification in Satellite Images by Injecting Traditional Features to CNN Models
论文作者
论文摘要
几年来，深度学习方法已成功地应用于遥感问题。在这些方法中，基于CNN的模型在使用卫星或空中图像解决土地分类问题方面具有很高的精度。尽管这些模型的精度很高，但通常具有较大的内存要求。另一方面，希望拥有用于应用程序的小型型号，例如在无人机上实施的应用程序，并且记忆空间较低。不幸的是，小型CNN型号与其大型版本那样不提供高精度。在这项研究中，我们提出了一种新颖的方法，可以通过向其注入传统特征来提高CNN模型的准确性，尤其是尺寸较小的方法。为了测试所提出方法的有效性，我们将其应用于CNN模型Squeezenet，Mobilenetv2，ShufflenetV2，VGG16和Resnet50V2，其大小为0.5 MB至528 MB。我们使用了样品平均，灰度级别共发生的矩阵特征，HU矩，局部二进制图案，定向梯度的直方图和颜色不变性作为传统的注入特征。我们在EuroSat数据集上测试了提出的方法，以执行土地分类。我们的实验结果表明，所提出的方法显着提高了土地分类精度，尤其是应用于小型CNN模型时。
Deep learning methods have been successfully applied to remote sensing problems for several years. Among these methods, CNN based models have high accuracy in solving the land classification problem using satellite or aerial images. Although these models have high accuracy, this generally comes with large memory size requirements. On the other hand, it is desirable to have small-sized models for applications, such as the ones implemented on unmanned aerial vehicles, with low memory space. Unfortunately, small-sized CNN models do not provide high accuracy as with their large-sized versions. In this study, we propose a novel method to improve the accuracy of CNN models, especially the ones with small size, by injecting traditional features to them. To test the effectiveness of the proposed method, we applied it to the CNN models SqueezeNet, MobileNetV2, ShuffleNetV2, VGG16, and ResNet50V2 having size 0.5 MB to 528 MB. We used the sample mean, gray level co-occurrence matrix features, Hu moments, local binary patterns, histogram of oriented gradients, and color invariants as traditional features for injection. We tested the proposed method on the EuroSAT dataset to perform land classification. Our experimental results show that the proposed method significantly improves the land classification accuracy especially when applied to small-sized CNN models.
