Change detection of satellite time series images based on spatial-temporal-spectral features
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摘要: 相较于常见的双时相遥感影像,时间序列遥感影像包含更丰富的地表信息,能够缓解“异物同谱”、“同物异谱”的影响,因而在变化检测中具有重要作用。但是目前时间序列遥感影像变化检测方法大多基于像素展开,忽略了像素和周围环境的空间关系,导致变化检测结果“噪声”现象明显。基于此,提出了一种基于时空谱特征的时间序列遥感变化检测算法(change detection based on spatial-temporal-spectral features, CDSTS)。首先,利用灰度共生矩阵和局部统计计算方法,从Landsat时间序列遥感影像中提取每个像素点的时间、空间(纹理和统计)和光谱特征; 其次,通过每个像素在不同波段上的时间序列表现规律,自动筛选出时序特征异常点,并与连续变化检测和分类法(continuous change detection and classification, CCDC)检测结果融合获取高精度变化/未变化训练样本点; 最后,利用上述样本点及其对应的时空谱特征训练支持向量机分类器,并基于该分类器对全图进行分类。结果表明,CDSTS算法在变化区域检测精准度方面明显优于常用的时间序列变化检测算法CCDC和土地扰动连续监测方法(continuous monitoring of land disturbance,COLD),总体精度提升了4.8~11.7百分点。Abstract: Compared with common dual-temporal satellite images, satellite time series images contain richer surface information and can alleviate the impact of foreign objects with the same spectrum and the same object with different spectra. Therefore, they play an important role in change detection. However, the change detection methods for satellite time series images are mostly based on pixels and ignore the spatial relationship between pixels and their surroundings. This causes noise in the change detection result. Accordingly, this study proposed a method of change detection based on spatial-temporal-spectral features(CDSTS) for satellite time series images. First, the temporal, spatial (textural and statistical), and spectral features of each pixel were extracted from Landsat time series images using a gray-level co-occurrence matrix and local statistical calculation methods. Then, anomalies of time series features were automatically screened according to the time series performance regularity of each pixel in different bands. These anomalies were then fused with the detection results of the continuous change detection and classification method (CCDC) to obtain high-precision changed/unchanged training sample points. Finally, the SVM classifier was trained using the training sample points and their corresponding spatial-temporal-spectral features for full graph classification. The results show that the CDSTS algorithm significantly outperforms the commonly used time series change detection algorithms CCDC and COLD (continuous monitoring of land disturbance) in terms of change detection precision, with the overall precision improved by 4.8 to 11.7 percentage points.
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Key words:
- Landsat /
- time series /
- change detection /
- textural feature
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