多光谱遥感水深反演及其水下碍航物探测技术

针对采用传统海洋测量手段难以获取危险及争议海区的水深和水下碍航物信息,提出了基于卫星遥感进行水深反演和水下碍航物探测的新思路。探讨了多光谱遥感水深反演的数据预处理方法、遥感水深反演模型的构建及水下碍航物遥感探测方法,并开展了相应的实验验证。实验表明,采用卫星遥感手段可以快速地获取测量船只不易到达海区的水深和水下碍航物信息,研究成果对海洋测绘技术的发展具有一定的指导意义和应用价值。     

增强型多时相云检测

针对云检测在高亮度地表以及雪覆盖区域存在过度检测的问题,设计了一种不依赖热红外波段的增强型多时相云检测EMTCD(Enhanced Multiple Temporal Cloud Detection)算法。首先,利用云的光谱特征建立单时相云检测规则,并基于云、雪的光谱差异构建了增强型云指数ECI(Enhanced Cloud Index),改进了云、雪的区分能力;其次,以同一区域无云影像为参考,基于ECI指数构建了多时相云检测算法,较好地克服了单时相云检测中高亮度地表、雪和云容易混淆的问题,提高了云检测的精度;最后,选择两个典型区域的Landsat-8 OLI影像,对比分析了不同算法的云检测结果。实验结果表明:ECI指数能够有效区分云、雪,EMTCD方法的平均检测精度达到93.2%,高于Fmask(Function of mask)(81.85%)、MTCD(Multi-Temporal Cloud Detection)(66.14%)和Landsat-8地表反射率产品LaSRC(Landsat-8 Surface Reflectance Code)的云检测结果(86.3%)。因此,本文提出的EMTCD云检测算法能够有效地减少高亮度地表和雪的干扰,实现不依赖热红外波段的高精度云检测。     

基于地理加权回归模型的水深反演算法研究

利用卫星多光谱数据反演浅海水深是水深测量的一种重要手段。已有水深反演方法是在研究区建立统一的数学参数的反演模型,未考虑由于海底底质和水质变化导致的空间非平稳性问题。本文使用地理加权回归模型(Geographically Weighted Regression, GWR)对回归参数在空间上进行估计,针对GWR模型的带宽对反演精度的影响,使用了交叉验证(Cross Validation,CV)的方法来确定最佳带宽,并以南海永兴岛和甘泉岛海域为实验区,基于WordiVew-2多光谱数据对使用GWR模型的可行性和精度进行了验证。实验结果表明:永兴岛研究区GWR模型精度较线性回归模型提高了36.05%,在0~5,5~10,10~15和15~20 m区间,精度分别提高了49.46%,39.97%,12.36%和49.68%;甘泉岛研究区GWR模型精度较线性回归模型提高了8.08%,在0~5,5~10,10~15和15~20 m区间,精度分别提高了12.05%,16.23%,4.49%和12.23%,表明GWR模型具有更好的水深反演效果。     

Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis

Abstract

This study proposes the development of a multi-sensor, multi-spectral composite from Landsat-8 and Sentinel-2A imagery referred to as ‘LSC’ for land use land cover (LULC) characterisation and compared with respect to the hyperspectral imagery of the EO1: Hyperion sensor. A three-stage evaluation was implemented based on the similarity observed in the spectral response, supervised classification results and endmember abundance information obtained using linear spectral unmixing. The study was conducted for two areas located around Dhundi and Rohtak in Himachal Pradesh and Haryana, respectively. According to the analysis of the spectral reflectance curves, the spectral response of the LSC is capable of identifying major LULC classes. The kappa accuracy of 0.85 and 0.66 was observed for the classification results from LSC and Hyperion data for Dhundi and Rohtak datasets, respectively. The coefficient of determination was found to be above 0.9 for the LULC classes in both the datasets as compared to Hyperion, indicating a good agreement. Thus, these three-stage results indicated the significant potential of a composite derived from freely available multi-sensor multi-spectral imagery as an alternative to hyperspectral imagery for LULC studies.     

基于选权迭代估计与非监督分类的多光谱图像变化检测

针对多光谱图像的变化检测问题,提出了一种基于选权迭代估计(iterative estimation with weight selection,IEWS)与非监督分类(unsupervised classification,UC)的多光谱图像变化检测方法。借鉴IEWS的思想,并以类似于迭代加权多元变化检测(iteratively reweighted multivariate alteration detection,IRMAD)的迭代模式进行回归估计,得到初步的变化检测结果;并通过对初始变化信息的UC处理,以及对不同类别的IEWS,得到最终的变化检测结果。利用该方法对TM图像进行了实验,结果表明:所得到的变化信息在空间位置上同该区域相应时间段内土地利用/覆盖的变化情况具有很好的一致性;同时与多元变化检测及IRMAD方法变化检测的结果相比较,表明该方法对相对较小的变化信息具有更好的变化检测能力。     

U-Net模型对不同空间分辨率防护林提取精度的影响

针对当前无人机影像获取精度高但数据规模小的问题,本文提出通过U-Net模型探讨不同空间分辨率对防护林提取精度的影响。以CW-20复合翼无人机搭载Micro MAC12 Snap多光谱传感器获取的300 m（空间分辨率0.15 m）、400 m（空间分辨率0.20 m）、500 m（空间分辨率0.25 m）3种不同高度的遥感影像为例,试验结果证明,3种不同高度的影像提取精度误差在1.3%以内;MIoU误差在3.7%以内。空间分辨率对防护林提取精度的影响较小,高空间分辨率的影像数据并不能显著提升防护林的提取精度。本文为大规模农林业遥感监测的数据源获取提供了理论依据。     

利用波段合成研究SPOT多光谱影像水体提取方法

SPOT多光谱影像仅有4个波段,光谱信息相对较少,通常的水体提取方法受到波段数的限制,通过波段合成增加SPOT多光谱影像可用的波段数,不仅增加了水体提取方法的种类,而且,通过对矿区塌陷地积水现象进行水体提取试验,结果表明：波段合成对SPOT影像的水体提取情况有一定程度的改善,水体提取更完整,误提现象较少。     

基于灰度-基元共生矩阵的多光谱遥感影像分类

提出了基于灰度-基元共生矩阵的遥感影像纹理分析的方法,分析了提取的纹理特征,实现了利用模糊C-均值算法对多光谱影像和纹理特征影像进行分类,比较和讨论了各种不同的分类结果.     

EDGE DETECTION METHOD OF REMOTE SENSING IMAGES BASED ON MATHEMATICAL MORPHOLOGY OF MULTI-STRUCTURE ELEMENTS

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise of remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.     

模拟多光谱的土壤重金属含量反演研究初探

根据多光谱传感器的光谱响应函数,采用实测ISI921VF反射光谱数据模拟Landsat卫星ETM+传感器多光谱数据,在模拟光谱的基础上,通过光谱特征提取、构建土壤指数对土壤重金属Cu,Pb,As进行预测分析。研究显示,Cu,Pb与模拟ETM+光谱的B2,B3波段显著相关,As与DSI,RSI,NDSI相关系数在0.6以上,基于模拟多光谱建立的Cu,As模型精度较高,平均相对误差分别为7.9%,2.7%,表明模拟的Landsat卫星ETM+传感器多光谱具有预测耕地土壤重金属的潜力,为实现大范围监测土壤重金属污染提供新思路。