Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused in the Nonsubsampled Contourlet Transform (NSCT) domain to generate high quality difference images. The NSCT is chosen as it is efficient in suppressing noise by utilizing its unique characteristics such as multidirectionality and shift-invariance that are suitable for change detection. The low frequency sub bands are fused by averaging to combine the complementary information in the two DIs, and, the higher frequency sub bands are merged by minimum energy rule, for preserving the edges and salient features in the image. By employing a novel Particle Swarm Optimization algorithm with Leader Intelligence (LIPSO), change maps are generated from fused sub bands in two different ways: (i) single spectral band, and (ii) combination of spectral bands. In LIPSO, the concept of leader and followers has been modified with intelligent particles performing Lévy flight randomly for better exploration, to achieve global optima. The proposed method achieved an overall accuracy of 99.64%, 98.49% and 97.66% on the three datasets considered, which is very high. The results have been compared with relevant algorithms. The quantitative metrics demonstrate the superiority of the proposed techniques over the other methods and are found to be statistically significant with McNemar’s test. Visual quality of the results also corroborate the superiority of the proposed method.

中文翻译：

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领导者智能PSO和NSCT特征融合的多光谱遥感图像变化检测

多年来，使用遥感影像进行变化检测（CD）一直是一个具有挑战性的问题。特别是在无人监管的领域，这甚至更加困难。提出了一种在无监督框架下的新颖的自动变化检测技术，以解决涉及遥感变化检测的实际挑战。由于变化图的准确性高度取决于差异图像（DI）的质量，因此在非下采样Contourlet变换（NSCT）域中融合了一组标准化的差异图像和互补的一组标准化比率图像，以生成高质量的差异图像。选择NSCT是因为它通过利用其独特的特性（例如适合变化检测的多方向性和位移不变性）来有效地抑制噪声。低频子带通过求平均来融合以组合两个DI中的互补信息，并且高频子带通过最小能量规则进行合并，以保留图像中的边缘和显着特征。通过采用具有领导者智能（LIPSO）的新颖粒子群优化算法，以两种不同方式从融合子带中生成变化图：（i）单个光谱带，和（ii）光谱带的组合。在LIPSO中，对领导者和跟随者的概念进行了修改，其中使用了智能粒子随机执行Lévy飞行，以进行更好的探索，以实现全局最优。所提出的方法在所考虑的三个数据集上实现了99.64％，98.49％和97.66％的总体准确性，这是非常高的。将结果与相关算法进行了比较。定量指标证明了所提出技术相对于其他方法的优越性，并且通过McNemar检验在统计学上具有显着意义。结果的视觉质量也证实了所提出方法的优越性。