A Stereo Matching Method for 3D Image Measurement of Long-Distance Sea Surface

Abstract

Tsunamis are some of the most destructive natural disasters. Some proposed tsunami measurement and arrival prediction systems use a limited number of instruments, then judge the occurrence of the tsunami, forecast its arrival time, location and scale. Since there are a limited number of measurement instruments, there is a possibility that large prediction errors will occur. In order to solve this problem, a long-distance tsunami measurement system based on the binocular stereo vision principle is proposed in this paper. The measuring range is 4–20 km away from the system deployment site. In this paper, we will focus on describing the stereo matching method for the proposed system. This paper proposes a two-step matching method. It first performs fast sparse matching, and then complete high precision dense matching based on the results of the sparse matching. A matching descriptor based on the physical features of sea waves is proposed to solve the matching difficulty caused by the similarity of sea surface image textures. The relationship between disparity and the y coordinate is built to reduce the matching search range. Experiments were conducted on sea surface images with different shooting times and distances; the results verify the effectiveness of the presented method.

Deep-Learning-Based Stereo Matching of Long-Distance Sea Surface Images for Sea Level Monitoring Systems

“…We solve this problem by adding a disparity initialization, which can produce the approximate location of the target sea wave in the right image based on the location of the detected sea wave in the left image. Disparity initialization is conducted within the sea surface image based on our previous work [55], assuming the pixel coordinate in the vertical direction is y. From the top of the image to the bottom, y increases, and we found that disparity d increases when y increases; in other words, the relationship between y and d is approximately proportional, as Figure 7 shows.…”

“…A Siamese CNN backbone is used to extract feature maps from template z and search image x. Template z is a cropped section of size h × w centered on the object obtained by a detection module. The search image x is a larger image centered on the estimated position of the object in another image; in this paper, it was obtained in our previous work [55]. To compare the difference between the template and search image, a depth-wise cross-correlation represented by ⋆d is selected to produce a multi-channel response.…”

Stereo matching method for 3D measurement of ocean wave surfaces

“…For example, Jiang Wenzheng built a mathematical model of wave characteristics based on the least square method and the wave surface theory, and proposed a measurement method of wave height without sea surface control points. Yang [1] Proposed a matching descriptor based on the physical characteristics of sea waves to solve the matching difficulties caused by the texture similarity of sea surface images, and established the relationship between parallax and y coordinate to reduce the matching search range. Lu [2] and others applied binocular stereo photography to bed shape measurement, providing high-precision data and effective technical methods for studying the macro motion characteristics of bed shape under different boundary conditions.…”