Object detection is an important task in remote sensing (RS) image analysis. To reduce the computational complexity of redundant information and improve the efficiency of image processing, visual saliency models are gradually being applied in this field. In this paper, a novel saliency detection method is proposed by exploring the sparse representation (SR) of, based on learning, contrast-weighted atoms (LCWA). Specifically, this paper uses the proposed LCWA atom learning formula on positive and negative samples to construct a saliency dictionary, and on nonsaliency atoms to construct a discriminant dictionary. An online discriminant dictionary learning algorithm is proposed to solve the atom learning formula. Then, we measure saliency by combining the coefficients of SR and reconstruction errors. Furthermore, under the proposed joint saliency measure, a variety of salient maps are generated by the discriminant dictionary. Finally, a fusion method based on global gradient optimisation is proposed to integrate multiple salient maps. Experimental results show that the proposed method significantly outperforms current state-of-the-art methods under six evaluation measures.
翻译:遥感图像分析是一项重要任务。为了减少冗余信息的计算复杂性和提高图像处理效率,正在逐步在这一领域应用视觉显著模型。在本文件中,通过探索基于学习的对比加权原子(LCWA)的稀疏代表(SR),提出了一种新的显著检测方法。具体地说,本文件使用拟议的LCWA原子学习公式,用正和负样本构建一个显微词典,用非对称原子构建一个显微字典。建议采用在线对称词典学习算法来解决原子学习公式。然后,我们通过将SR系数与重建错误合并来测量显著特征。此外,根据拟议的联合突出度测量,根据共分辨词典生成了各种突出的地图。最后,建议采用基于全球梯度优化的混合法,将多个突出的地图整合在一起。实验结果显示,拟议的方法大大优于六种评估措施下的当前状态方法。