围填海疑点疑区监测技术方法

为严格围填海管控,迅速、准确和及时掌握用海情况,文章在明确围填海疑点疑区类型的基础上,概述其监测技术方法。研究结果表明:围填海疑点疑区的监测内容主要包括用海位置、面积、方式和类型等;主要利用卫星遥感、航空遥感和地面核查等技术手段;监测流程主要包括数据收集和处理、遥感变化图斑提取、疑点疑区图斑筛选以及疑点疑区核查4个步骤;监测成果可制作围填海疑点疑区核查报告和图集,为海洋行政主管部门提供执法和管理依据。

An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image

Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.

基于RANet互导融合学习的遥感影像浒苔检测方法

针对遥感影像浒苔检测标注数据少的问题,本文提出一种基于残差注意力网络(residual attention network,RANet)互导融合学习的浒苔检测方法。首先,本文搭建了残差卷积模块联合注意力机制的RANet模型用于浒苔检测。其次,在双网络架构下利用两个RANet模型相互引导,挑选双模型融合后的高置信度伪标签,结合数据增强来逐步扩充训练集,从而对双模型迭代学习实现高精度的浒苔检测。实验结果表明,与阈值法、生成对抗网络(generative adversarial network, GAN)、经典分割模型(FCN、SegNet、UNet、PSPNet和DeepLabv3+)相比,基于RANet互导融合学习的浒苔检测方法具有更高的检测准确性。本研究构建的模型具备进行大规模浒苔监测的可行性,可为大规模浒苔暴发时的灾情监测提供技术支撑。

高分一号的浮筏养殖信息提取方法

针对浮筏海水养殖遥感信息自动提取业务化应用难度较大的问题,该文提出一种光谱、纹理特征结合的支持向量机(SVM)算法,高效提取海水、养殖物。基于国产高分一号(GF-1)高分辨率卫星影像数据,以江苏省连云港近岸海域为研究区域,以海水、养殖物作为为分类对象,采用主成分变换筛选信息量大,较好地保留浮筏水产养殖信息的前8个主成分分量开展分类实验。实验结果表明:将纹理特征应用于浮筏养殖物信息提取方法中,可以提高养殖物分类精度,与最大似然估计、最小距离方法分类方法比较,总体分类精度提升了3%~5%。研究结果可为浮筏养殖用海遥感监测平台构建提供基础技术支持。

基于自动阈值分类的高分辨率影像海陆分界线自动提取

海陆分界线的变化对滩涂面积和湿地系统变化具有指示作用并且影响航运活动,因而需提取海陆分界线。视海陆分界线为陆地(包括淤泥质潮滩)和海水(包括河道)的瞬时分界线。首先,构建近红外—绿—蓝波段的假彩色图像,进行色彩、对比度增强和双边滤波处理;其次,构建海陆分界线提取指数(Sea-land Boundary Extraction Index,SBEI),并利用自动阈值分析方法,将影像分为海洋和大陆,同时合并陆地碎斑。针对河道伸入陆地的问题,对于宽度为W的河口,采用步长为W+1的膨胀腐蚀的方法消除;最后用Canny算子提取海陆分界线。为验证算法的有效性,选取1m分辨率的高分二号卫星影像,对3种海岸类型进行实验,结果表明此方法的提取精度约为5个像元,相较NDWI和CV模型的提取精度至少提高10个像元。