无限水深聚焦波完全非线性数值模拟

基于势流理论提出一种新的高阶边界元方法对无限水深的聚焦波浪进行完全非线性数值模拟.自由水面满足完全非线性边界条件,模拟波浪的非线性效果可以达到更高阶.利用镜像原理,建立一种全新的格林函数应用到无限水深的数值波浪水槽中,以致于两无限深水槽侧壁的积分可以被排除.为了产生相应的入射波和吸收出流波浪,一个由点源组成的造波装置被布置于计算域内,同时人工阻尼层被用来吸引出流波浪,由波浪聚焦的方法得到极限波浪.通过开展线性和完全非线性聚焦波浪的数值实验及与理论解对比,验证本数值模型可以用来模拟无限深水域的极限波浪,且在出流边界没有反射.     

基于单景高分辨率光学卫星遥感影像的浅海水下地形提取

本文基于海浪波折射现象和浅水波理论,提出了一种基于单景高分辨率光学遥感影像的浅海地形提取方法。首先,基于浅水波理论推导出适用于浅海区域的水深与海浪波长、频率的定量关系,针对近岸光学遥感图像复杂的海浪特征,讨论了两种海浪波长提取方法,即FFT方法和剖面线法。然后提出了基于长距离波长波动分析的海浪频率计算方法,解决了单景遥感影像的波浪频率计算难题。最后,利用单景QuickBird高分辨率光学遥感影像,以海南岛三亚湾为研究区域进行了应用实验,结果表明,对12m以浅的浅海区域,在不需要任何辅助参数的情况下,反演获得了浅海地形(DEM),经与1:25000比例尺海图的水深对比验证,地形趋势吻合良好,反演水深的均方根误差为1.07m,相对水深误差为16.2%,表明该方法适合于浅海水下地形的提取,且具有无需实测水深数据和环境参数的支持的优点。     

基于滩涂围垦的海岛城市时空扩张特征研究——以舟山本岛为例

以舟山本岛为研究单元,利用1995年,2005年和2015年的遥感影像,采用生态学分析、空间统计学分析、城市扩张指数等方法来探究舟山本岛滩涂围垦活动和城市用地扩张的特点,并分析两者之间的关联。结果表明:(1)在1995—2015年的20 a间,滩涂围垦的面积不断扩大。(2)1995—2005年的滩涂资源利用以近陆域的海涂为主,2005—2015年逐渐向浅海发展。(3)1995—2015年间,城市用地面积前10 a增长强度为0.53,增长速度为3.01 km²/a,后10 a增长强度为1.46,增长速度为8.11 km²/a。(4)舟山本岛的城市发展重心在20 a间经历了一个自西向东移动而后向北跳跃的过程,各区域间协同发展,使舟山本岛经济整体性和城市功能合理性得到提升。(5)滩涂围垦形成的土地与城市用地扩张之间存在着正相关关系,且从研究数据来看两者之间发生自然演替需要时间,大约为8~10 a,人工干预可以大幅缩短这个过程所需要的时间。     

基于小波变换和HIS变换的海冰SAR与光学遥感影像融合方法研究

Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral information, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be improved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transformation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar(ASAR) loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensity-saturation(HIS) space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue(RGB) space, and the optical image from the China-Brazil Earth Resources Satellite(CBERS-02B) was transferred from the RGB space to the HIS space. The principal component analysis(PCA) method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.     

基于主成分分析和决策级融合的高光谱图像分类方法研究

为了评价主成分分析方法和决策级数据融合方法对高光谱图像地物分类结果的影响,作者利用各分类器和主成分分量的优势,提出了两组地物分类结果的决策级融合策略,利用8种常用监督分类方法对高光谱原始图像和PCA变换后不同分量组合图分类得到的结果进行决策级融合,并应用覆盖黄河入海口新老河道交界处的CHRIS/PROBA高光谱图像开展实验研究。结果表明:直接采用每类地物分类精度最高的、空穴和缝隙采用总体分类精度最高的融合策略,在综合考虑生产者精度和用户精度的情形下,仅使用最大似然法、支持向量机和人工神经网络3种分类方法,按照分类精度从高到低的顺序进行的融合分类效果最好,总体分类精度为87.82%。与8种监督分类方法中效果最好的最大似然法相比,精度提高了2.7个百分点,同时明显减少了错分现象,尤其是对于分布面积较小的翅碱蓬和柽柳,滩涂被误分为翅碱蓬、芦苇被误分为柽柳的现象大大降低。     

基于遗传小波神经网络的海底声学底质识别分类

分割海底声纳探测图像,提取单元特征向量进行主成份分析,选取均值、标准差、对比度、相关系数、能量及同质性作为训练特征向量,构建小波神经网络。利用遗传算法优化小波神经网络的初始权值及小波参数,对砂、礁石、泥3种底质类型分别进行训练,并得到3种底质的测试精度都在90%以上,优于单独利用小波神经网络进行训练时的测试精度,克服了小波神经网络训练时易陷入局部极小的固有缺陷,表明基于遗传算法的小波神经网络可有效用于海底底质声纳图像的识别和分类。     

基于声纳图像纹理特征的海底底质分类方法研究

一般不同底质的海底声纳图的纹理特征不同。重点分析了不同底质声纳图像纹理特征的差异,为底质分类的实现提供了基础。并提取了海底声纳图像的灰度共生矩阵,将灰度共生矩阵的统计特征作为特征向量。最后应用自组织迭代(ISODATA)分类算法依据对特征向量进行分类,从而实现了海底底质的划分。     

Observer presence may alter the behaviour of reef fishes associated with coral colonies

Although underwater visual census (UVC) is the most frequently used technique for quantifying reef fish assemblages, remote video analysis has been gaining attention as a potential alternative. In the South Atlantic Ocean, Millepora spp. (class Hydrozoa) are the only branching coral species; however, little is known about the ecological role that they play for reef fish communities. We compared these two observation methods (remote video and UVC) to estimate reef fish abundance and species richness associated with colonies of the fire‐coral Millepora alcicornis at Tamandaré Reefs, Northeast Brazil. Additionally, the two different techniques were used to compare species behaviour in association with fire‐corals in order to examine the biases associated with each technique and provide useful information for behavioural ecologists studying fish–coral associations. There were no differences in reef fish abundance or species richness sampled by remote video or UVC. However, a significant difference in the behaviour of associated fish was recorded between the two methods. In the presence of a diver carrying out a UVC, fish were observed spending more time sheltered amongst the coral branches compared with passively swimming on coral colonies with the remote video technique. Specifically, on the remote video recordings agonistic interactions between fish and passive swimming accounted for 33.3% and 22.2% of the census time, respectively. By comparison, when observed by a diver fish spent 34.8% of their time sheltering amongst the coral branches. We demonstrate that both techniques are similarly effective for recording fish abundance and species richness associated with fire‐corals. However, differences were observed in the ability of each method to detect the behaviour of coral‐associated fishes. Our findings show that behavioural ecologists studying complex fish–coral associations need to ensure that their aims are clearly defined and that they choose the most appropriate technique for their study in order to minimize methodological biases.     

风暴浪作用下沙质岸滩稳定机制物理模型试验研究

近年来风暴潮等海洋灾害日趋频发,沙质海岸侵蚀问题也愈发突出,沙滩稳定防护显得日益重要。为研究风暴浪作用下沙质岸滩稳定机制问题,设计了一系列的水槽试验,对风暴浪作用下沙质岸滩的稳定机制和演变过程进行了录像观察和研究分析。试验中采用图像处理技术,根据水和岸滩床面的像素值差异,对岸滩整体剖面进行实时动态提取;对比和分析了不同入射波高、波周期、水深、岸滩初始坡度以及波高连续变化下沙质岸滩演变过程。试验结果表明,岸滩稳定与岸滩初始坡度和沙坝的发育直接相关,而波参数主要影响岸滩扰动幅度和沙坝以及前滩侵蚀边界的位置变化。当入射波高连续变化时,沙坝迅速响应并向离岸迁移。岸滩变化幅值与入射波能流存在明显正相关关系,波能流越大对岸滩稳定性的危害越大。而水位升高会增强前滩向岸侵蚀风险。此外,在本试验尺度下,前滩以侵蚀为主。当岸滩初始坡度小于稳定坡度且波陡较小时,即Dean参数Ω''较小时,岸滩才发生明显的前滩淤积,这对于试验尺度下岸滩恢复工况研究至关重要。具体来说,当岸滩整体坡度为1:10且前滩坡度达到1:5~1:2.5时,岸滩稳定性最好,岸滩形态最接近最终平衡剖面,岸滩趋于稳定的时间最短。     

遥感模式分类中的空间统计学应用——以面向对象的遥感影像农田提取为例

如何有效地从遥感图像中提取所需信息,是遥感图像处理和应用的关键,而尺度选择问题一直是影响遥感信息提取精度的关键问题之一。本文论述了利用空间统计学方法解决遥感影像模式分类中的尺度问题的理论基础。针对面向对象影像分析问题,将影响遥感影像多尺度分割的尺度分割参数概括为空间属性分割参数、光谱属性分割参数和影像对象面积阈值参数,并分别提出了基于统计学的尺度参数估计方法。以SPOT-5影像面向对象农田提取为例,基于变异函数方法进行了尺度优选试验,系列尺度分类试验结果表明基于空间统计学尺度估计得到的尺度分割结果进行分类能得到最高的精度,进而证明了基于空间统计学方法进行面向对象信息提取尺度估计的有效性。该方法是完全数据驱动的方法,基本不需要先验知识参与。不同于以往分割后评价的尺度选择方法会占用大量计算资源且耗费大量时间,本文提出的方法不仅能在一定程度上保证面向对象信息提取的精度,而且在一定程度上也提高了面向对象信息提取的效率和自动化程度。