Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification

Abstract

Abstract:Ship detection by Unmanned Airborne Vehicles (UAVs) and satellites plays an important role in a spectrum of related military and civil applications. To improve the detection efficiency, accuracy, and speed, a novel ship detection method from coarse to fine is presented. Ship targets are viewed as uncommon regions in the sea background caused by the differences in colors, textures, shapes, or other factors. Inspired by this fact, a global saliency model is constructed based on high-frequency coefficients of the multi-scale and multi-direction wavelet decomposition, which can characterize different feature information from edge to texture of the input image. To further reduce the false alarms, a new and effective multi-level discrimination method is designed based on the improved entropy and pixel distribution, which is robust against the interferences introduced by islands, coastlines, clouds, and shadows. The experimental results on optical remote sensing images validate that the presented saliency model outperforms the comparative models in terms of the area under the receiver operating characteristic curves core and the accuracy in the images with different sizes. After the target identification, the locations and the number of the ships in various sizes and colors can be detected accurately and fast with high robustness.

Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

“…On average, the detection time of our method is 1.6 s. The efficiency of our algorithm is not the highest. Compared with the method from [13], which has comparable accuracy, our efficiency is almost double. In Table 1, we can see that with the increasing amount of interference, the accuracy of our method decreases slightly from Group 1 to Group 3.…”

“…On average, the detection time of our method is 1.6 s. The efficiency of our algorithm is not the highest. Compared with the method from [13], which has comparable accuracy, our efficiency is almost double. A linear function combining pixel and region characteristics was employed to select ship candidates in [7].…”

“…At the same time, we compared our method with state-of-the-art methods proposed in [7], [13], and [17]. Table 2 was obtained by averaging the evaluation indices under different sea conditions, showing the average performance of the four methods.…”

“…Table 3 shows a comparison of four methods under different sea conditions. The precision of the methods from [17] and [13] is high with respect to calm sea surfaces, but for complex sea surfaces, detection performance is greatly reduced, as shown in Table 3. The method proposed in this paper is optimal for detection accuracy.…”

“…Dong proposed a novel visual saliency detection method based on differences in statistical characteristics to locate ROIs [12]. Xu presented a global saliency model based on high-frequency coefficients of multi-scale and multi-direction wavelet decomposition for ship detection [13]. Generally speaking, the processing results of the aforesaid algorithms were unsatisfactory with respect to panchromatic images.…”