自适应空间邻域分析和瑞利-高斯分布的多时相遥感影像变化检测

Adaptive spatial neighborhood analysis and Rayleigh-Gauss distribution fitting for change detection in multi-temporal remote sensing images

提出了一种基于自适应空间邻域分析和瑞利-高斯模型(Rayleigh-Gaussmodels,RGM)分布的多时相遥感影像自动变化检测方法。该方法把自适应空间邻域信息和改进的差值影像与比值影像乘积变换融合法(improved multiplying transform fusion,IMTF)结合构造差异影像,可以有效地抑制噪声和消除多时相影像之间配准误差的影响,具有更强的鲁棒性。在对差异影像的分割处理中,运用瑞利和高斯模型分别模拟变化类像元和非变化类像元的分布情况,然后估计出两类像元的概率密度参数,最后采用改进的KI(Kittler-Illingworth)阈值选择算法自动高效地确定最佳变化检测阈值,提取变化区域。通过对模拟的和真实的MTRSI数据集的实验表明所提出的方法是有效的和鲁棒的。

This paper proposes a novel automatic change detection approach for single band multi-temporal remote sensing images （MTRSI）. First, the difference image is constructed by combining the spatial neighborhood information with the improved multiplying transform fusion （MTF） technique, which can well weaken noises and eliminate the effects caused by the registration error of multi-temporal images. In the segmentation processing of the difference image, the distributions of changed and unchanged classes are fitted by Rayleigh-Ganss models （RGM） and the probability densities of changed and unchanged pixels are estimated. Then the optimal change detection threshold is calculated automatically and effectively by the improved Kittler-Illingworth （KI） threshold selection algorithm. Finally, the changed regions are extracted. The experimental results obtained on the simulated MTRSI and the real MTRSI confirmed the effectiveness of the proposed approach. In particular, the results in terms of overall error and overall detected accuracy proved that the proposed generation approach of the difference image could have better performance than the MTF technique. In addition, as expected, the RGM was proved to be more suitable than the Gauss models （GM） and the Generalized-Gauss models （GGM） to fit the distributions of changed and unchanged classes And the change detection experiments also confirmed that the proposed automatic threshold selection method based on RGM fitting technique could achieve the very similar performance to the optimal results exhibited by the supervised manual trial and error procedure （MTEP）.