This letter presents a novel local feature descriptor, entitled “scale-orientation robust infrared features” (SORIF), specifically designed to be robust to geometric variations, particularly to rotations, while dealing with the nonmonotonic intensity variations between images from the visible (VIS) and infrared (IR) spectra. The method calculates the keypoint’s orientation using phase congruency, rotates the window for alignment, and then extracts texture and edge features using Log-Gabor filters. These features are compiled into a single vector, forming a descriptor that effectively handles texture and maintains consistency across different orientations of the image, showcasing its robustness in addressing geometric issues in remote-sensing images. We evaluated the proposed descriptor using four different datasets, extensively used in previous works and composed of images taken from the visible and IR spectra. The experimental results revealed that the proposed descriptor is robust to VIS/IR images’ nonmonotonic intensity variations and geometric changes. Moreover, it had a superior matching performance, outperforming some state-of-the-art algorithms.

中文翻译：

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基于纹理和相位一致性的可见光-红外图像匹配的方向鲁棒局部特征描述符

这封信提出了一种新颖的局部特征描述符，名为“尺度方向鲁棒红外特征”（SORIF），专门设计用于对几何变化（特别是旋转）具有鲁棒性，同时处理可见光（VIS）图像之间的非单调强度变化和红外 (IR) 光谱。该方法使用相位一致性计算关键点的方向，旋转窗口进行对齐，然后使用 Log-Gabor 滤波器提取纹理和边缘特征。这些特征被编译成单个向量，形成一个描述符，可以有效处理纹理并保持图像不同方向的一致性，展示其在解决遥感图像中的几何问题方面的鲁棒性。我们使用四个不同的数据集评估了所提出的描述符，这些数据集在之前的工作中广泛使用，并由从可见光和红外光谱拍摄的图像组成。实验结果表明，所提出的描述符对于可见光/红外图像的非单调强度变化和几何变化具有鲁棒性。此外，它具有卓越的匹配性能，优于一些最先进的算法。