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Low-resolution Ship Detection from High-altitude Aerial Images

机译：从高空航空影像中进行低分辨率船舶探测

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Ship detection from optical images taken by high-altitude aircrafts such as unmanned long-endurance airships and unmanned aerial vehicles has broad applications in marine fishery management, ship monitoring and vessel salvage. However, the major challenge is the limited capability of information processing on unmanned high-altitude platforms. Furthermore, in order to guarantee the wide detection range, unmanned aircrafts generally cruise at high altitudes, resulting in imagery with low-resolution targets and strong clutters suffered by heavy clouds. In this paper, we propose a low-resolution ship detection method to extract ships from these high-altitude optical images. Inspired by a recent research on visual saliency detection indicating that small salient signals could be well detected by a gradient enhancement operation combined with Gaussian smoothing, we propose the facet kernel filtering to rapidly suppress cluttered backgrounds and delineate candidate target regions from the sea surface. Then, the principal component analysis (PCA) is used to compute the orientation of the target axis, followed by a simplified histogram of oriented gradient (HOG) descriptor to characterize the ship shape property. Finally, support vector machine (SVM) is applied to discriminate real targets and false alarms. Experimental results show that the proposed method actually has high efficiency in low-resolution ship detection.
机译：从高空飞行器（例如无人驾驶长寿飞艇和无人驾驶飞行器）拍摄的光学图像中进行船舶检测在海洋渔业管理，船舶监控和船舶打捞中具有广泛的应用。但是，主要挑战是无人高空平台上信息处理的能力有限。此外，为了确保宽广的检测范围，无人驾驶飞机通常会在高空航行，从而导致图像分辨率低，目标浓密，杂乱无章。在本文中，我们提出了一种低分辨率的船只检测方法，以从这些高空光学图像中提取船只。受最近对视觉显着性检测的研究（表明可以通过梯度增强操作结合高斯平滑度很好地检测到小显着信号）的启发，我们提出采用小面核滤波来快速抑制杂乱的背景并从海面描绘出候选目标区域。然后，使用主成分分析（PCA）计算目标轴的方向，然后使用定向梯度（HOG）描述符的简化直方图来表征船的形状特性。最后，应用支持向量机（SVM）来区分真实目标和错误警报。实验结果表明，该方法在低分辨率舰船检测中具有很高的效率。

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