Salient object detection (SOD) in optical remote sensing images (ORSIs) has become increasingly popular recently. Due to the characteristics of ORSIs, ORSI-SOD is full of challenges, such as multiple objects, small objects, low illuminations, and irregular shapes. To address these challenges, we propose a concise yet effective Texture-Semantic Collaboration Network (TSCNet) to explore the collaboration of texture cues and semantic cues for ORSI-SOD. Specifically, TSCNet is based on the generic encoder-decoder structure. In addition to the encoder and decoder, TSCNet includes a vital Texture-Semantic Collaboration Module (TSCM), which performs valuable feature modulation and interaction on basic features extracted from the encoder. The main idea of our TSCM is to make full use of the texture features at the lowest level and the semantic features at the highest level to achieve the expression enhancement of salient regions on features. In the TSCM, we first enhance the position of potential salient regions using semantic features. Then, we render and restore the object details using the texture features. Meanwhile, we also perceive regions of various scales, and construct interactions between different regions. Thanks to the perfect combination of TSCM and generic structure, our TSCNet can take care of both the position and details of salient objects, effectively handling various scenes. Extensive experiments on three datasets demonstrate that our TSCNet achieves competitive performance compared to 14 state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/TSCNet.

翻译：光学遥感图像（ORSIs）中的显著目标检测（SOD）近年来日益受到关注。由于ORSIs的特性，ORSISOD面临诸多挑战，如多目标、小目标、低照度及不规则形状等。为解决这些问题，我们提出一种简洁而有效的纹理-语义协作网络（TSCNet），用于探索纹理线索与语义线索在ORSI-SOD中的协作机制。具体而言，TSCNet采用通用编码器-解码器结构。在编码器和解码器之外，TSCNet包含一个关键的纹理-语义协作模块（TSCM），该模块对从编码器提取的基础特征进行有价值的特征调制与交互。TSCM的核心思想是充分利用最低层的纹理特征和最高层的语义特征，增强显著区域在特征上的表达。在TSCM中，我们首先利用语义特征增强潜在显著区域的位置信息，然后使用纹理特征渲染并恢复目标细节。同时，我们还感知不同尺度的区域，并构建区域间的交互。得益于TSCM与通用结构的完美结合，我们的TSCNet能够兼顾显著目标的位置与细节，有效处理各类场景。在三个数据集上的大量实验表明，与14种最先进方法相比，我们的TSCNet取得了具有竞争力的性能。方法和结果代码已开源：https://github.com/MathLee/TSCNet。