基于多源遥感时空谱特征融合的滑坡灾害检测方法

针对多源遥感图像中滑坡时空谱特征融合利用模式单一、检测识别性能差等问题，提出了一种基于多源遥感时空谱特征融合的滑坡灾害检测方法，以滑坡发生地区灾前灾后多源遥感图像为基础，通过对多波段遥感图像进行光谱空间和尺度空间配准，构建融合时序变化特征、光谱特征和空间形状特征遥感影像数据集，将多维多谱变化信息转化为光谱表征模型.利用支持向量机模型对滑坡目标和背景地物进行识别，在此基础上根据滑坡基础形状模型的轴向长宽比、面积参数和不变矩等典型形状特征指标对滑坡区域进行目标精确分类与识别.实际实验表明，该方法能够达到95%的识别率，优于多种常见滑坡遥感检测方法.

Multi-Source Remote Sensing Based Accurate Landslide Detection Leveraging Spatial-Temporal-Spectral Feature Fusion

Accurate landslide detection is extremely important in emergency rescue. Aiming at the problems concerning current landslide remote sensing detection that the fusion and utilization of spatial, temporal, and spectral features are poor in target detection model and the accuracy of recognition is unsatisfied, in this paper, we propose an accurate landslide detection method based on multi-source remote sensing images, leveraging the fusion of spatial, temporal, and spectral features. In specific, we construct a new multi-bands remote sensing image dataset, exploiting the registration of spectral and scale spaces based on the remote sensing image before and after landslide. Moreover, we combine the features of temporal variation, spectrum and spatial shape, which are transformed into the spectral representational model. And then, the support vector machine (SVM) algorithm is used to identify the landslide objects based on this new image dataset and representational model. Furthermore, we use the typical shape features, such as the axial aspect ratio, area and invariant moment, which are extracted for the fundamental shape models of landslides, to further classify these landslide objects. Finally, we conduct extensive experiments to evaluate the performance of the propose method by comparing with baseline methods. The experimental results show that our method outperforms the baseline algorithms, while achieving up to 95% accuracy in landslide detection.