ABSTRACT

An improved graph-cut-based change detection method is proposed in this paper to make full use of the spectral and spatial information from multispectral remote-sensing images. The proposed method detects changes by minimizing the graph-cut energy function. The energy function consists of change feature energy and image feature energy. The two features are generated based on spectral information and spatial-context information, respectively. Change feature energy item is calculated from the change vector, which uses the spectral information to detect changes. Image feature energy item is obtained by calculating the similarity of the texture features between neighbouring pixels. The image feature energy item uses spatial information to refine the contours of change detection results and remove false alarms (FA). A novel energy function is proposed to quantify the spatial-context information and measure the difference information between multispectral images. Finally, the max-flow/min-cut method is employed to produce the final change map by minimizing the energy function. The experiments carried out on medium- and high-resolution images demonstrate the robustness and effectiveness of the proposed method. This study provides a new perspective for incorporating spectral and spatial information in change detection.

摘要

提出了一种改进的基于图割的变化检测方法，以充分利用多光谱遥感图像的光谱和空间信息。所提出的方法通过最小化图形切割能量函数来检测变化。能量函数由变化特征能量和图像特征能量组成。分别基于频谱信息和空间上下文信息生成这两个特征。根据变化向量计算变化特征能量项，该变化向量使用光谱信息来检测变化。通过计算相邻像素之间纹理特征的相似度来获得图像特征能量项。图像特征能量项使用空间信息来细化变化检测结果的轮廓并消除错误警报（FA）。提出了一种新颖的能量函数来量化空间上下文信息并测量多光谱图像之间的差异信息。最后，采用最大流量/最小切割方法通过最小化能量函数来产生最终变化图。在中分辨率和高分辨率图像上进行的实验证明了该方法的鲁棒性和有效性。这项研究为将光谱和空间信息纳入变化检测提供了新的视角。在中分辨率和高分辨率图像上进行的实验证明了该方法的鲁棒性和有效性。这项研究为将光谱和空间信息纳入变化检测提供了新的视角。在中分辨率和高分辨率图像上进行的实验证明了该方法的鲁棒性和有效性。这项研究为将光谱和空间信息纳入变化检测提供了新的视角。