基于Relief-PCA特征选择的遥感图像变化检测

面向对象的变化检测技术在高分辨率遥感图像领域已经得到广泛地应用。由于遥 感图像受光照、大气环境等成像条件的影响，图像特征的质量也参差不齐，筛选出高质量的特 征成为对象级遥感图像变化检测的关键。针对此问题，提出了一种基于 Relief-PCA 特征选择的 对象级遥感图像变化检测方法。首先，对原始图像进行多尺度分割获得目标对象，并提取对象 的光谱特征与纹理特征；然后，利用对数比值法获得变化矢量，再使用 Relief-PCA 特征选择的 方法对图像的对象特征进行筛选与降维；最后，计算并生成 CVA 变化强度图，利用 Otsu 方法 对变化强度图进行阈值分割得到最终的变化检测结果。实验表明：与已有方法相比，该方法的 变化检测精度更高，误检率和漏检率更低。

Remote Sensing Image Change Detection Based on Relief-PCA Feature Selection

Object-oriented change detection technology has been widely used in the field of high-resolution remote sensing images. As the remote sensing images are affected by imaging conditions such as illumination, atmospheric environment and other factors, the quality of image features also varies. Selecting high-quality features becomes the key of the change detection of remote sensing image at the object level. For the above problems, a change detection method of object-level remote sensing images based on Relief-PCA feature selection has been proposed. In the proposed method, first of all, the original image is multi-scaled to obtain the target object. Afterwards, the spectral features and texture features of the object are extracted. Then a logarithmic ratio method is used to obtain the change vector, and the object features of the original image are filtered and dimensioned through the Relief-PCA feature selection method. Finally, the change vector analysis (CVA) variation intensity map is calculated and generated. The Otsu method is used to conduct the threshold segmentation of the variation intensity map to obtain the final change detection result. Experimental results show that compared with other state-of-the-art methods, the proposed method has higher detection accuracy, lower misdetection rate and lower missed detection rate.