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.
Highlights
In public and martial applications, recognition of aircraft type from remotely sensed imageries has more importance
We reduce the redundant information and irrelevant features by using the principal component analysis (PCA) technique [23, 24]
We present a methodology for aircraft classification by using deep learning techniques
Summary
In public and martial applications, recognition of aircraft type from remotely sensed imageries has more importance. In the early stages of researches, handcrafted features, like "SIFT" [4, 5]and "HOG" [6], are some of the approaches that were used for the recognition of objects from remote sensing images such as aircraft, boats, houses and so on. Numerous methodologies are based on shape matching methods [7, 8], like the grouping of an edge potential and artificial bee colony (ABC) methodology in [8] and the coarse-to-fine, suggested in [7] by employing the parametric shape representation These technologies play a key part in the presentation improvements of aircraft recognition/acknowledgment. After applying some prior processing on aircraft images, we apply handcrafted and some convolutional neural network algorithms on MTARSI dataset to improve classification accuracy. Different classifiers are applied, and the best result is compared with existing techniques
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