Abstract Object detection in very high resolution (VHR) optical remote sensing (RS) images is one of the most fundamental but challenging tasks in the field of RS image analysis. To reduce the computational complexity of redundant information and improve the efficiency of image processing, visual saliency models have been widely applied in this field. In this paper, a novel saliency detection model based on Contrast-weighted Dictionary Learning (CDL) is proposed for VHR optical RS images. Specifically, the proposed CDL learns salient and non-salient atoms from positive and negative samples to construct a discriminant dictionary, in which a contrast-weighted term is proposed to encourage the contrast-weighted patterns to be present in the learned salient dictionary while discouraging them from being present in the non-salient dictionary. Then, we measure the saliency by combining the coefficients of the sparse representation (SR) and reconstruction errors. Furthermore, by using the proposed joint saliency measure, a variety of saliency maps are generated based on the discriminant dictionary. Finally, a fusion method based on global gradient optimization is proposed to integrate multiple saliency maps. Experimental results on four datasets demonstrate that the proposed model outperforms other state-of-the-art methods.

中文翻译：

---

基于对比加权字典学习的 VHR 光学遥感图像显着性检测

摘要 超高分辨率 (VHR) 光学遥感 (RS) 图像中的目标检测是 RS 图像分析领域最基本但最具挑战性的任务之一。为了降低冗余信息的计算复杂度，提高图像处理的效率，视觉显着性模型在该领域得到了广泛的应用。在本文中，针对 VHR 光学 RS 图像提出了一种基于对比加权字典学习（CDL）的新型显着性检测模型。具体来说，所提出的 CDL 从正样本和负样本中学习显着和非显着原子以构建判别字典，其中提出了一个对比加权项来鼓励对比加权模式出现在学习到的显着字典中，同时阻止它们从出现在非显着字典中。然后，我们通过结合稀疏表示（SR）和重构误差的系数来测量显着性。此外，通过使用所提出的联合显着性度量，基于判别字典生成了各种显着性图。最后，提出了一种基于全局梯度优化的融合方法来整合多个显着图。在四个数据集上的实验结果表明，所提出的模型优于其他最先进的方法。