One main challenge in detecting built-up land cover changes using synthetic aperture radar (SAR) instruments is that complicated backscattering behaviours and the superimposition of speckles on rich textures cause a large number of false alarms. Using trajectory-based analyses from time-series SAR imagery can mitigate false alarms since the temporal variability in backscattering during construction improves discrimination capability. This paper presents an approach towards the detection of built-up land change based on a single-channel SAR stack. The proposed methodology includes the generation of a change indicator, the Markov modelling procedure and the delineation of changes over built-up areas. The generation of the change indicator aims to provide a feature with abundant contrast between changed and stable areas, a high signal-to-noise ratio and detail preservation. To this end, all temporal information is converted into a map of the coefficient of variation. After error removal, this change detector is combined with a Markov random field (MRF) criterion function. Rather than MRF modelling by iteration with very complex stochastic models, we propose using SAR temporal trajectory under a hypothesis test framework and interferometric coherence series to establish conditional density for each class. Then, the Graph-cuts theory is applied to delineate the boundary between changed and stable areas, followed by a binary classification procedure based on speckle divergence to exclude natural areas. The technique is tested on both synthetic data and two TerraSAR-X datasets covering representative areas with rich texture. We found that in a complex built environment that is challenging for classical change indicators and state-of-the-art techniques, the presented method can provide smaller overall error with better detail preservation.

使用合成孔径雷达（SAR）仪器检测堆积土地覆盖变化的一个主要挑战是复杂的反向散射行为以及斑点在丰富纹理上的叠加会导致大量错误警报。使用来自时间序列SAR图像的基于轨迹的分析可以减轻误报的发生，因为在施工过程中反向散射的时间变化可提高判别能力。本文提出了一种基于单通道SAR堆栈的已建成土地变化检测方法。拟议的方法包括变更指标的生成，马尔可夫建模程序以及对建成区变更的描述。更改指标的生成旨在提供一种功能，使更改后的区域与稳定的区域之间具有丰富的对比，高信噪比和细节保留。为此，所有时间信息都被转换成变化系数的图。消除错误后，此变化检测器与Markov随机字段（MRF）标准函数组合。我们建议在假设检验框架和干涉相干序列下使用SAR时间轨迹，而不是通过非常复杂的随机模型通过迭代进行MRF建模，以为每个类别建立条件密度。然后，应用图割理论来描述变化区域和稳定区域之间的边界，然后基于斑点散度进行二值分类，以排除自然区域。在合成数据和两个TerraSAR-X数据集（覆盖具有丰富纹理的代表性区域）上测试了该技术。