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
this https URL

基于纹理 - 语义协作网络（TSCNet），我们在光学遥感图像中探索纹理线索和语义线索的协作，提出了一种简洁而有效的显著目标检测方法（ORSI-SOD）来应对多个目标、小目标、低照明和不规则形状等挑战，并通过 TSCNet 在三个数据集上的大量实验验证了其竞争性能。

纹理 - 语义协作网络用于 ORSI 显著目标检测

Texture-Semantic Collaboration Network for ORSI Salient Object Detection

Existing methods for Salient Object Detection in Optical Remote Sensing
Images (ORSI-SOD) mainly adopt Convolutional Neural Networks (CNNs) as the
backbone, such as VGG and ResNet. Since CNNs can only extract features within
certain receptive fields, most ORSI-SOD methods generally follow the
local-to-contextual paradigm. In this paper, we propose a novel Global
Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the
global-to-local paradigm. Specifically, GeleNet first adopts a transformer
backbone to generate four-level feature embeddings with global long-range
dependencies. Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial
Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local
interactions, and a Knowledge Transfer Module (KTM) to further enhance
cross-level contextual interactions. D-SWSAM comprehensively perceives the
orientation information in the lowest-level features through directional
convolutions to adapt to various orientations of salient objects in ORSIs, and
effectively enhances the details of salient objects with an improved attention
mechanism. SWSAM discards the direction-aware part of D-SWSAM to focus on
localizing salient objects in the highest-level features. KTM models the
contextual correlation knowledge of two middle-level features of different
scales based on the self-attention mechanism, and transfers the knowledge to
the raw features to generate more discriminative features. Finally, a saliency
predictor is used to generate the saliency map based on the outputs of the
above three modules. Extensive experiments on three public datasets demonstrate
that the proposed GeleNet outperforms relevant state-of-the-art methods. The
code and results of our method are available at
this https URL

基于全局到局部范式，提出了一种用于光学遥感图像中显著目标检测的全局提取局部探索网络 (GeleNet)。GeleNet 首先采用 Transformer 骨干网络生成四级特征嵌入以捕获全局远程依赖性。然后，GeleNet 利用方向感知的混洗加权空间注意力模块 (D-SWSAM) 和其简化版本 (SWSAM) 增强本地交互，并利用知识传输模块 (KTM) 进一步增强跨级上下文交互。最后，根据上述三个模块的输出，使用显著性预测器生成显著性图。对三个公共数据集进行的广泛实验证明所提出的 GeleNet 优于相关最先进方法。