The appearance of ships is easily affected by external factors—illumination, weather conditions, and sea state—that make ship classification a challenging task. To facilitate realization of enhanced ship-classification performance, this study proposes a ship classification method based on multi-feature fusion with a convolutional neural network (CNN). First, an improved CNN characterized by shallow layers and few parameters is proposed to learn high-level features and capture structural information. Second, handcrafted features of the histogram of oriented gradients (HOG) and local binary patterns (LBP) are combined with high-level features extracted by the improved CNN in the last fully connected layer to obtain discriminative feature representation. The handcrafted features supplement the edge information and spatial texture information of the ship images. Then, the Softmax function is used to classify different types of ships in the output layer. Effectiveness of the proposed method is evaluated based on its application to two datasets—one self-built and the other publicly available, called visible and infrared spectrums (VAIS). As observed, the proposed method demonstrated attainment of average classification accuracies equal to 97.50% and 93.60%, respectively, when applied to these datasets. Additionally, results obtained in terms of the F1-score and confusion matrix demonstrate the proposed method to be superior to some state-of-the-art methods.
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