TY - JOUR A2 - 张，清尘AU - 张，程AU - 他，丹PY - 2020 DA - 2020年5月8日TI - 一个深的多尺度融合法通过低等级的稀疏分解为对象显基于视觉的检测在城市数据光学遥感影像SP - 7917021 VL - 2020 AB - 城市数据提供了丰富的可承受生活和工作的人的信息。在这项工作中，我们研究光学遥感图像的物体显着性检测，这有利于城市场景的解释。显着性检测与所述遥感图像的重要信息，这严重模仿人的视觉系统选择的区域。它在其他图像处理功能强大的作用。它已经成功地在变化检测，目标跟踪，温度逆转，和其他任务的巨大成就。传统的方法有一些缺点，如耐用性差，计算复杂度高。因此，本文提出了通过低秩稀疏分解为在光学遥感图像对象显着性检测深多尺度融合方法。首先，我们的遥感图像进行多尺度分割。然后，我们计算的显着性值，并生成建议区域。 The superpixel blocks of the remaining proposal regions of the segmentation map are input into the convolutional neural network. By extracting the depth feature, the saliency value is calculated and the proposal regions are updated. The feature transformation matrix is obtained based on the gradient descent method, and the high-level semantic prior knowledge is obtained by using the convolutional neural network. The process is iterated continuously to obtain the saliency map at each scale. The low-rank sparse decomposition of the transformed matrix is carried out by robust principal component analysis. Finally, the weight cellular automata method is utilized to fuse the multiscale saliency graphs and the saliency map calculated according to the sparse noise obtained by decomposition. Meanwhile, the object priors knowledge can filter most of the background information, reduce unnecessary depth feature extraction, and meaningfully improve the saliency detection rate. The experiment results show that the proposed method can effectively improve the detection effect compared to other deep learning methods. SN - 1530-8669 UR - https://doi.org/10.1155/2020/7917021 DO - 10.1155/2020/7917021 JF - Wireless Communications and Mobile Computing PB - Hindawi KW - ER -