While image registration has been studied in remote sensing community for decades, registering multimodal data [e.g., optical, light detection and ranging (LiDAR), synthetic aperture radar (SAR), and map] remains a challenging problem because of significant nonlinear intensity differences between such data. To address this problem, this paper presents a fast and robust matching framework integrating local descriptors for multimodal registration. In the proposed framework, a local descriptor, such as Histogram of Oriented Gradient (HOG), Local Self Similarity (LSS), or Speeded-Up Robust Feature (SURF), is first extracted at each pixel to form a pixel-wise feature representation of an image. Then we define a similarity measure based on the feature representation in frequency domain using the 3 Dimensional Fast Fourier Transform (3DFFT) technique, followed by a template matching scheme to detect control points between images. In this procedure, we also propose a novel pixel-wise feature representation using orientated gradients of images, which is named channel features of orientated gradients (CFOG). This novel feature is an extension of the pixel-wise HOG descriptors, and outperforms that both in matching performance and computational efficiency. The major advantage of the proposed framework includes: (1) structural similarity representation using the pixel-wise feature description and (2) high computational efficiency due to the use of 3DFFT. Experimental results on different types of multimodal images show the superior matching performance of the proposed framework than the state-of-the-art methods. Moreover, we design an automatic registration system for very large-size multimodal images (more than 20000*20000 pixels) based on the proposed framework. Experimental results show the effectiveness of the designed registration system.The matlab code is available in this manuscript.
翻译:虽然几十年来遥感界一直在研究图像登记问题,但由于这些数据之间非线性强度差异很大,因此在遥感界注册多式联运数据[如光学、光探测和测距(LiDAR)、合成孔径雷达(SAR)和地图]仍是一个具有挑战性的问题。为了解决这一问题,本文件提出了一个快速和强有力的匹配框架,将本地描述器整合为多式登记。在拟议的框架中,一个本地描述器,如Orient Gradient(HOG)、本地自相近(LSS)或快速提升 Robust Feat(SURF)框架,首次在每像素中提取一个具有挑战性的特点,以便形成图像的像素显示(SARDAR)的特征表示。这个新的描述基于频率域的特征表示法,使用3TFourier变变换(3DFFFT)技术,然后用模板匹配方法来检测图像之间的控制点。在这个程序里,我们还提议使用图像的直方向梯度框架(CFLODF)的频道特征特征显示(CFDGO)的频道特征特征特征特征特征特征特征特征特征,这个在2000年的高级图中,在结构结构变压结构变压结构结构结构结构结构结构结构结构结构结构结构中展示中显示一个功能结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构中显示一个扩展结构结构结构结构结构结构结构结构结构的延伸。