  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.
