Texture-Semantic Collaboration Network for ORSI Salient Object Detection
Gongyang Li Zhen Bai Zhi Liu
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.