Recently, salient object detection in optical remote sensing images (RSI-SOD) has attracted great attention. Benefiting from the success of deep learning and the inspiration of natural SOD task, RSI-SOD has achieved fast progress over the past two years. However, existing methods usually suffer from the intrinsic problems of optical RSIs: 1) cluttered background; 2) scale variation of salient objects; 3) complicated edges and irregular topology. To remedy these problems, we propose a hybrid feature aligned network (HFANet) jointly modeling boundary learning to detect salient objects effectively. Specifically, we design a hybrid encoder by unifying two components to capture global context for mitigating the disturbance of complex background. Then, to detect multiscale salient objects effectively, we propose a Gated Fold-ASPP (GF-ASPP) to extract abundant context in the deep semantic features. Furthermore, an adjacent feature aligned module (AFAM) is presented for integrating adjacent features with unparameterized alignment strategy. Finally, we propose a novel interactive guidance loss (IGLoss) to combine saliency and edge detection, which can adaptively perform mutual supervision of the two subtasks to facilitate detection of salient objects with blurred edges and irregular topology. Adequate experimental results on three optical RSI-SOD datasets reveal that the presented approach exceeds 18 state-of-the-art ones. All codes and detection results are available at https://github.com/lyf0801/HFANet.

中文翻译：

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用于光学遥感图像中显着目标检测的混合特征对齐网络


近年来，光学遥感图像中的显着目标检测（RSI-SOD）引起了人们的广泛关注。受益于深度学习的成功和自然SOD任务的启发，RSI-SOD在过去两年取得了快速进展。然而，现有方法通常存在光学 RSI 的固有问题：1）杂乱的背景； 2）显着物体的尺度变化； 3）复杂的边缘和不规则的拓扑。为了解决这些问题，我们提出了一种混合特征对齐网络（HFANet）联合建模边界学习以有效地检测显着对象。具体来说，我们通过统一两个组件来设计一种混合编码器来捕获全局上下文，以减轻复杂背景的干扰。然后，为了有效地检测多尺度显着目标，我们提出了门控折叠ASPP（GF-ASPP）来提取深层语义特征中的丰富上下文。此外，提出了相邻特征对齐模块（AFAM），用于将相邻特征与非参数化对齐策略集成。最后，我们提出了一种新颖的交互式引导损失（IGLoss），将显着性和边缘检测结合起来，它可以自适应地执行两个子任务的相互监督，以方便检测具有模糊边缘和不规则拓扑的显着目标。对三个光学 RSI-SOD 数据集的充分实验结果表明，所提出的方法超过了 18 个最先进的方法。所有代码和检测结果均可在 https://github.com/lyf0801/HFANet 上获取。