## 1 Introduction
This kind of method requires the artificial design of multi-dimensional description features and then classification by machine learning. For instance, [PERSON] combined the LBP descriptor of the image texture with the ship structure and used the adaptive boosting (AdaBoost) algorithm to generate hypotheses for ships. The ideal detection results were obtained for large-scale ships in high resolution images of calm sea surfaces; however, there was no difference in LBP distribution between each part of the ship structure at low resolution and in complex backgrounds, and the performance was degraded [17]. [PERSON] designed a ship histogram of an oriented gradient (S-HOG) descriptor to obtain rotation invariance and applied principal component analysis (PCA) to compute the orientation. However, the complexity of the algorithm was high [18]. [PERSON] used the learning algorithm based on AdaBoost classification to extract histograms of an oriented gradient (HOG) for distinguishing targets and false alarms [19]. [PERSON] employed the compactness and length-width ratio to remove false alarms. The two kinds of features related to shape description, and the method lacked sufficient strong features [7]. It is important to design and select distinguished features. The other method can detect features automatically via deep learning models. [PERSON] used a convolutional neural network (CNN) based on GPU acceleration to design feature layers of different depths, and completed real-time detection for \(704\times 704\) images [20]. [PERSON] proposed a CNN-based renormalization method to improve the quality of object proposal with respect to very high resolution (VHR) remote sensing images [21]. [PERSON] used a ship detection algorithm based on a transfer-learned single shot multi-box detector (SSD). The SSD fully utilizes the feature expression of each convolution layer [22]. Deep learning has been proven to fit big data and object detection extremely well in such databases as ImageNet (a large-scale visual database for visual object recognition). Moreover, large-scale weight parameters in the network can consume a great deal of computing and memory resources; for example, the AlexNet model (a deep convolution neural network proposed by [PERSON]) exceeds 200 MB, and the VGG-16 (the visual geometry group model) goes beyond 500 MB. The hardware migration of this kind of method is complicated and is not suitable for real-time processing onboard. Hence, it is crucial to conduct in-depth research on large-field and panchromatic remote sensing images in various complex conditions for fast and effective ship detection. Aiming at these addressed problems, a novel approach based on visual saliency and multi-dimensional descriptor is proposed. The three stages in the framework are similar to those in [23]: sea-land segmentation, ROI extraction, and target discrimination, as shown in Figure 1. However, the last two steps have been improved. In the second stage, a visual saliency model based on an improved PQFT algorithm is constructed. Firstly, the image spatial correlation feature, the contrast feature, and the multi-direction feature are considered to establish the quaternion. Secondly, a hyper-complex Fourier transform is executed. Gaussian filtering of different scales is performed on the transformed result to synthesize the best saliency map. Finally, an adaptive binary segmentation of ROIs based on GrabCut (interactive foreground extraction using iterated graph cuts) is used to locate candidates. The presented saliency model has a high detection accuracy and obtains more complete target contours, regardless of the variety of scenes. In the last stage, the rotation-invariant modified LBP feature is constructed to make up for the poor discrimination of different regions caused by the lack of contrast description in the original LBP. At the same time, shape, texture, and moment-invariant features are extracted and concatenated as a feature description for SVM classification to eliminate false alarms and identify targets.