Deep learning-based aerial image classification model using inception with residual network and multilayer perceptron

At present times, unmanned aerial vehicles (UAVs) have become popular owing to the advantages such as scalable, versatile, autonomous, and inexpensive. The aerial image classification process has attracted more attention among the UAV-enabled surveillance system facilitating objective recognition, tracking process and considerably improving the visual surveillance results. This paper introduces effective deep learning-based aerial image classification using Inception with Residual Network v2 and multilayer perceptron (DLIRV2-MLP). The presented DLIRV2-MLP method includes a series of operations such as pre-processing, feature extraction, and classification. Primarily, the UAVs are used for the collection of aerial images by the use of imaging sensors exist in it. Followed by, the Inception with ResNet v2 based feature extractor is utilized to derive a useful set of feature vectors from the aerial image. Finally, the classification of aerial images using the derived feature vectors takes place by the use of MLP model. Here, the MLP classifier is used as the final layer of the Inception with ResNet v2 model in place of the softmax layer to improve the classification performance. The efficacy of the DLIRV2-MLP method is validated using a benchmark aerial image dataset and the resultant experimental values highlighted the effectiveness of the DLIRV2-MLP method with 90% and above in accuracy and precision. The problem of existing method faces some issues in computation whereas the aerial image classification with DLIRV2-MLP gives efficient computation while comparing with the existing method.