海底微地形粗糙度作为海底沉积物重要的物理性质,对于海洋工程以及海洋科学考察都有着重要意义,如何利用光学理论进行海底微地形粗糙度测量,是近年来该领域研究关注的热点。基于光学中的从明暗恢复形状(shame from shading,SFS)算法,提...海底微地形粗糙度作为海底沉积物重要的物理性质,对于海洋工程以及海洋科学考察都有着重要意义,如何利用光学理论进行海底微地形粗糙度测量,是近年来该领域研究关注的热点。基于光学中的从明暗恢复形状(shame from shading,SFS)算法,提出一种快速的海底微地形粗糙度测量算法,在模型构建同时,添加水下光传播时的吸收和衰减模型,测量出海底的微地形,并用幂律形式进行参数拟合,以表征粗糙度。仿真证明该算法具有95%的置信度,是一种适用于海底微地形粗糙度测量的光学算法,并经过实验验证,证明其有效性和正确性。展开更多
The multibeam sonars can provide hydrographic quality depth data as well as hold the potential to provide calibrated measurements of the seafloor acoustic backscattering strength. There has been much interest in utili...The multibeam sonars can provide hydrographic quality depth data as well as hold the potential to provide calibrated measurements of the seafloor acoustic backscattering strength. There has been much interest in utilizing backscatters and images from multibeam sonar for seabed type identification and most results are obtained. This paper has presented a focused review of several main methods and recent developments of seafloor classification utilizing multibeam sonar data or/and images. These are including the power spectral analysis methods, the texture analysis, traditional Bayesian classification theory and the most active neural network approaches.展开更多
文摘海底微地形粗糙度作为海底沉积物重要的物理性质,对于海洋工程以及海洋科学考察都有着重要意义,如何利用光学理论进行海底微地形粗糙度测量,是近年来该领域研究关注的热点。基于光学中的从明暗恢复形状(shame from shading,SFS)算法,提出一种快速的海底微地形粗糙度测量算法,在模型构建同时,添加水下光传播时的吸收和衰减模型,测量出海底的微地形,并用幂律形式进行参数拟合,以表征粗糙度。仿真证明该算法具有95%的置信度,是一种适用于海底微地形粗糙度测量的光学算法,并经过实验验证,证明其有效性和正确性。
文摘The multibeam sonars can provide hydrographic quality depth data as well as hold the potential to provide calibrated measurements of the seafloor acoustic backscattering strength. There has been much interest in utilizing backscatters and images from multibeam sonar for seabed type identification and most results are obtained. This paper has presented a focused review of several main methods and recent developments of seafloor classification utilizing multibeam sonar data or/and images. These are including the power spectral analysis methods, the texture analysis, traditional Bayesian classification theory and the most active neural network approaches.