Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods.

中文翻译：

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基于深度残差密集网络的光学遥感图像中粗到细的舰船检测新方法

光学遥感图像中的自动船舶检测由于其在海上安全和渔业控制中的广泛应用而具有重要意义。大多数船舶检测算法都使用单波段图像来设计低级和手工制作的功能，这些功能很容易受到云和强波等干扰的影响，并且对于大型船舶变化而言并不稳健。在本文中，我们提出了一种基于离散小波变换（DWT）和深度残差密集网络（DRDN）的新颖的从粗到细船检测方法，以解决这些问题。首先，采用多光谱图像进行海域分割，并使用增强的DWT快速提取具有尽可能低缺失警报的船舶候选区域。其次，将具有清晰空间细节的全色图像用于船舶分类。特别，我们提出了局部残差密集块（LRDB），以通过局部残差连接和密集连接的卷积层完全提取语义特征。DRDN主要由四个LRDB组成，旨在进一步消除错误警报。此外，我们采用了多类分类策略，该策略可以克服目标之间的巨大类内差异，并识别不同大小的船只。大量实验表明，该方法在复杂图像背景下具有很高的鲁棒性，并且比其他现有技术具有更高的检测精度。我们采用多类别分类策略，该策略可以克服目标之间的巨大类别差异并识别不同大小的船只。大量实验表明，该方法在复杂图像背景下具有很高的鲁棒性，并且比其他现有技术具有更高的检测精度。我们采用多类别分类策略，该策略可以克服目标之间的巨大类别差异并识别不同大小的船只。大量实验表明，该方法在复杂图像背景下具有很高的鲁棒性，并且比其他现有技术具有更高的检测精度。