Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

Abstract

Automatic ship detection by Unmanned Airborne Vehicles (UAVs) and satellites is one of the fundamental challenges in maritime research due to the variable appearances of ships and complex sea backgrounds. To address this issue, in this paper, a novel multi-level ship detection algorithm is proposed to detect various types of offshore ships more precisely and quickly under all possible imaging variations. Our object detection system consists of two phases. First, in the category-independent region proposal phase, the steerable pyramid for multi-scale analysis is performed to generate a set of saliency maps in which the candidate region pixels are assigned to high salient values. Then, the set of saliency maps is used for constructing the graph-based segmentation, which can produce more accurate candidate regions compared with the threshold segmentation. More importantly, the proposed algorithm can produce a rather smaller set of candidates in comparison with the classical sliding window object detection paradigm or the other region proposal algorithms. Second, in the target identification phase, a rotation-invariant descriptor, which combines the histogram of oriented gradients (HOG) cells and the Fourier basis together, is investigated to distinguish between ships and non-ships. Meanwhile, the main direction of the ship can also be estimated in this phase. The overall algorithm can account for large variations in scale and rotation. Experiments on optical remote sensing (ORS) images demonstrate the effectiveness and robustness of our detection system.

Ship Detection in Multispectral Satellite Images Under Complex Environment

“…For comparison, six algorithms were implemented and trained with training set II and then tested on test set III. The algorithms used were: the dense scale-invariant feature transform (denseSIFT) [40] with SVM [41]; local binary pattern [42] with SVM; histogram of oriented gradient (HOG) with SVM, as some methods used new derivatives of HOG as the feature extractor [43,44]; the method of Reference [9] (denoted as cdDNN) and SVDNet [17] with band 4 as its input; and YOLT [20] with RGB color image as its input. Figure 10 displays the precision-recall curves of these four methods and the proposed method in this paper (denoted as Spec + LFN).…”

Abnormal activity detection in surveillance video scenes

“…For tracking any object, feature extraction plays a significant role. Combined (Harris and Scale Invariant Feature Transform (SIFT)) which is proposed in this system to extract features of objects that reduction of dimensionality and reduce the number of resources needed to describe a large range of data [15][16][17]. Tracking objects detected in frames sequence and matching them is a critical stage of intelligent security systems because of the ability to extract the objects and analyze their behavior [18].…”

Arbitrary-Oriented Ship Detection Based on RetinaNet for Remote Sensing Images

“…For example, He et al [4] proposed a method for ship detection using pose consistency voting. Dong et al [5] generated candidate regions by using multiscale analysis, and then used a rotation-invariant descriptor to distinguish between ships and non-ships. Traditional methods mostly rely heavily on manual features, so the detection performance is generally not strong.…”