侧扫声呐的规范图像表示评估
Evaluation of a Canonical Image Representation for Sidescan Sonar
AI总结：
针对侧扫声呐图像因AUV高度、目标距离和传感器分辨率导致的畸变问题，提出结合强度校正与斜距校正的规范变换方法，并在HUGIN AUV真实数据上验证其能提升图像块相似性与SIFT匹配精度。
摘要：
声学传感器在自主水下航行器（AUV）中发挥着重要作用。侧扫声呐（SSS）探测范围广，并能提供高分辨率的照片级真实感图像。然而，SSS将三维海底投影为二维图像，这些图像会因AUV的高度、目标的距离以及传感器的分辨率而产生畸变。因此，同一物理区域在不同测线的SSS图像中可能呈现出显著的视觉差异，给像素对应和模板匹配等任务带来困难。本文提出了一种由强度校正和斜距校正组成的规范变换方法，以减小上述畸变。强度校正包括波束方向图校正和利用三种不同朗伯定律（cos、cos²、cot）的入射角校正，而斜距校正则去除天底区，并将SSS元素的位置投影到等水平间距、与视点无关的区间中。所提方法在HUGIN AUV采集的真实数据上进行了评估，以人工标注的像素对应作为真值参考。基于图像块对的实验结果比较了相似性度量和关键点描述子匹配。结果表明，规范变换能够提高图像块相似性，并在同一物理区域被声波照射的不同图像中提高SIFT描述子匹配精度。
英文摘要：
Acoustic sensors play an important role in autonomous underwater vehicles (AUVs). Sidescan sonar (SSS) detects a wide range and provides photo-realistic images in high resolution. However, SSS projects the 3D seafloor to 2D images, which are distorted by the AUV's altitude, target's range and sensor's resolution. As a result, the same physical area can show significant visual differences in SSS images from different survey lines, causing difficulties in tasks such as pixel correspondence and template matching. In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion. The intensity correction includes beam pattern correction and incident angle correction using three different Lambertian laws (cos, cos2, cot), whereas the slant range correction removes the nadir zone and projects the position of SSS elements into equally horizontally spaced, view-point independent bins. The proposed method is evaluated on real data collected by a HUGIN AUV, with manually-annotated pixel correspondence as ground truth reference. Experimental results on patch pairs compare similarity measures and keypoint descriptor matching. The results show that the canonical transformation can improve the patch similarity, as well as SIFT descriptor matching accuracy in different images where the same physical area was ensonified.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
