针对传统的迭代条件模式(iterated conditional model,ICM)算法应用于遥感影像分割时容易出现离散斑块和孤立点的问题,提出了一种基于马尔科夫随机场(Markov random field,MRF)的改进ICM遥感影像分割算法。首先,在获取初始标记之前加入...针对传统的迭代条件模式(iterated conditional model,ICM)算法应用于遥感影像分割时容易出现离散斑块和孤立点的问题,提出了一种基于马尔科夫随机场(Markov random field,MRF)的改进ICM遥感影像分割算法。首先,在获取初始标记之前加入保边去噪效果良好的双边滤波器(bilateral filter,BF),用于遥感影像的预处理;并用多阈值最大类间方差法(Otsu)获取初始标记,以克服传统的初始标记获取算法中K-means聚类算法类别数不确定和算法复杂度不易控制以及错分现象明显等问题;然后,利用MRF描述像元的空间相关性,形成顾及上下文信息的ICM遥感影像分割算法。通过遥感影像数据分割实例验证,所提方法的分割精度优于传统的ICM算法。展开更多
In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the bas...In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.展开更多
基金Supported by the National Natural Science Foundation of China (No. 61172047)
文摘In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.
