变分法遥感影像人工地物自动检测

人工地物（建筑物、道路、桥梁等）检测是目标识别的一个重要组成部分。本文将人工地物检测转换为能量泛函数最优化问题。首先对遥感影像进行超像素分割，综合图像的颜色、纹理、梯度等信息，以超像素为单元计算图像的显著度信息，然后构建一个包含显著性约束、面积和边界约束、纹理约束及灰度方差约束的能量泛函数，通过变分法迭代求解能量泛函最小值，获取目标前景部分即为人工地物区域。本文以重庆和广东某地的遥感影像数据为例对算法进行验证，将其与常见的人工地物目标提取算法，如C-V模型、MRF模型，以及当下研究较为热门的深度学习算法进行对比。试验结果表明，该算法能有效地检测出遥感影像中的人工地物区域，并保证较低的误检率及漏检率。论文对该方法与深度学习方法进行了一定的分析对比。

A Variational Approach for Automatic Man-made Object Detection from Remote Sensing Images

Man-made object detection is important for object detection from remote sensing images.In this paper we propose a variational approach for man-made object detection which formulates the man-made object detection problem as a problem of variational energy optimization.In this method,an image is firstly segmented into superpixels,and the saliency map by combining image features such as texture,color and gradient is computed.In second step,we construct an energy function with saliency,area,edge,texture and intensity variance constrains.The energy function is solved via variational method to obtain the foreground,which is the detected man-made objects.The proposed approach on several remote sensing images is evaluated and compared with the C-V model,MRF model and deep learning based semantic segmentation.Experimental results show that the proposed approach can effectively detect man-made objects on remote sensing images with low false alarm and false negatives rates.The comparison and analysis with deep learning based method are also presented.