A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images
A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images
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DOI：
10.1016/j.neucom.2020.05.108
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Chongyi Li;Runmin Cong;Chunle Guo;Hua Li;Chunjie Zhang;Feng Zheng;Yao Zhao
中科院分区：
文献类型：
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作者：
Chongyi Li;Runmin Cong;Chunle Guo;Hua Li;Chunjie Zhang;Feng Zheng;Yao Zhao
光学遥感图像空间分辨率的多样性、地物类型、尺度和方位的多样性以及背景的复杂性对现有的显著地物检测方法提出了挑战。通常，直接将针对自然场景图像（NSIs）设计的SOD方法应用于RSIs是不令人满意的。本文提出了一种新的并行上下融合网络（PDF-Net）用于光学遥感影像中的SOD，该网络充分利用了光路中的低层和高层特征以及跨光路的多分辨率特征来区分不同尺度的显著目标并抑制杂乱的背景。具体来说，保持一个关键的观察，即无论图像的分辨率如何，显着的物体仍然是显着的，PDF-Net采取连续的下采样，以形成五个平行的路径，并感知光学RSI中常见的缩放显着物体。同时，我们采用密集连接，以利用低，高层次的信息在同一条路径，并建立交叉路径的关系，这显式地产生强大的功能表示。最后，我们将多分辨率特征融合在并行路径中，以联合收割机结合不同分辨率特征的优点，即，高分辨率特征由完整的结构和清晰的细节组成，而低分辨率特征突出缩放的显著对象。在ORSSD数据集上进行的大量实验表明，该网络在定性和定量上都优于现有的方法，具有上级性能。
The diverse spatial resolutions, various object types, scales and orientations, and cluttered backgrounds in optical remote sensing images (RSIs) challenge the current salient object detection (SOD) approaches. It is commonly unsatisfactory to directly employ the SOD approaches designed for nature scene images (NSIs) to RSIs. In this paper, we propose a novel Parallel Down-up Fusion network (PDF-Net) for SOD in optical RSIs, which takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds. To be specific, keeping a key observation that the salient objects still are salient no matter the resolutions of images are in mind, the PDF-Net takes successive down-sampling to form five parallel paths and perceive scaled salient objects that are commonly existed in optical RSIs. Meanwhile, we adopt the dense connections to take advantage of both low- and high-level information in the same path and build up the relations of cross paths, which explicitly yield strong feature representations. At last, we fuse the multiple-resolution features in parallel paths to combine the benefits of the features with different resolutions,i.e., the high-resolution feature consisting of complete structure and clear details while the low-resolution features highlighting the scaled salient objects. Extensive experiments on the ORSSD dataset demonstrate that the proposed network is superior to the state-of-the-art approaches both qualitatively and quantitatively.