Texture-Semantic Collaboration Network for ORSI Salient Object Detection

计算机科学 编码器 突出 特征(语言学) 人工智能 自编码 对象(语法) 模式识别(心理学) 计算机视觉 纹理(宇宙学) 语义特征 构造(python库) 职位(财务) 图像(数学) 人工神经网络 哲学 语言学 财务 经济 程序设计语言 操作系统
作者
Gongyang Li,Zhen Bai,Zhi Li
出处
期刊:IEEE Transactions on Circuits and Systems Ii-express Briefs [Institute of Electrical and Electronics Engineers]
卷期号:: 1-1
标识
DOI:10.1109/tcsii.2023.3333436
摘要

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.
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