Abstract
Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a region of interest from input images. In order to efficiently match ship candidates, the principle of our approach is to distinguish suspected areas from the images based on hue, saturation, value (HSV) differences between ships and the background. The whole approach is the basis of an experiment with a large ship dataset, consisting of Google Earth images and HRSC2016 datasets. The experiment shows that the H-YOLO network, which uses the same weight training from a set of remote sensing images, has a 19.01% higher recognition rate and a 16.19% higher accuracy than applying the you only look once (YOLO) network alone. After image preprocessing, the value of the intersection over union (IoU) is also greatly improved.
Highlights
Over the past few years, the object detection domain has rapidly improved, opening many valuable opportunities to detect ships in maritime environments
We introduce a novel vessel remote sensing image classification network, the so-called HSV-you only look once (YOLO) network, consisting of two essential components: an HSV-operation module and a one-stage detection module
Compared with the two-stage target detection algorithms represented by faster RCNNs, one-stage algorithms directly provide category and location information through the backbone network instead of using a region proposal network (RPN)
Summary
Over the past few years, the object detection domain has rapidly improved, opening many valuable opportunities to detect ships in maritime environments. Li et al [6] introduced a deep feature-based method to detect ships in very high-resolution optical remote sensing images and to improve the faster R-CNN thanks to a multi-scale approach. A common issue all the above methods still need to address appropriately is the extraction and processing of RoIs. In particular, in order to detect the strip-like rotated assembled object, which is a common issue when processing remote sensing images, a few related works introduced some novel methods to tackle the target’s recognition issue [32,33]. Extracting high-quality RoIs can achieve better detection outcomes since this might suppress one of the main drawbacks of two-stage algorithms, as well as being potentially computationally more efficient. The main principle is to apply the difference of HSV color space among vessels’ remote sensing pictures to extract the RoI and to send them into the YOLOv3 network.
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