Abstract
The characterization of aircraft in remote sensing satellite imagery has many armed and civil applications. For civil purposes, such as in tragedy and emergency aircraft searching, airport scrutiny and aircraft identification from satellite images are very important. This study presents an automated methodology based on handcrafted and deep convolutional neural network (DCNN) features. The presented aircraft classification technique consists of the following steps. The handcrafted features achieved from a local binary pattern (LBP) and DCNN are fused by feature fusion techniques. The DCNN features are extracted from Alexnet and Inception V3. Then we adopted a feature selection technique called principal component analysis (PCA). PCA removes the redundant and irrelevant information and improves the classification performance. Then, Famous supervised methodologies categorize these selected features. We chose the best classifier based on its highest accuracy. The proposed technique is executed on the multi-type aircraft remote sensing images (MTARSI) dataset, and the overall highest accuracy that we achieved from our proposed method is 96.8% by the linear support vector machine (SVM) classifier.