秦皇岛市海岸线遥感提取及变化监测

监测海岸线的变化是海岸带管理、海平面变化和海岸带演变研究中一项非常重要的任务,遥感技术凭借其高时效、大范围、低成本等特点正逐渐成为监测海岸线变化的重要手段。以秦皇岛市的海岸为例,结合海岸类型和潮位校正对高分辨率SPOT卫星影像进行海岸线提取,并利用同时期海岸线实地测量数据对海岸线提取进行精度验证,结果表明：使用该方法提取的海岸线精度较高。利用该方法得到研究区2006\,2009和2011年海岸线提取结果,通过叠加多时相海岸线监测其变化情况,并采用基线法定量分析北戴河砂质海岸线的变化,结果与实际情况相符。     

遥感与DEM相结合的海岸线高精度提取方法

针对海岸线现场测量难度大、施测周期长等问题,研究了应用高分辨率卫星遥感影像和DEM数据提取海岸线的技术方法。以SPOT 5融合影像为例,给出了基岩、砾石、砂质、人工4种类型海岸线的解译标志建立方法和提取原则,其中砂质岸线的提取须对影像做拉伸处理;针对纯遥感手段提取海岸线的不足,提出结合精细DEM数据提取海岸线的几种方式,分析了基于砂质岸线解译标志提取海岸线高程精度的可靠性,并给出了结合DEM数据提取基岩岸线的应用实例。通过应用海岸线现场测量结果开展精度验证,海岸线提取结果的定位精度优于5 m,可满足1∶10 000比例尺的制图精度要求。     

基于面向对象方法的沙化土地遥感信息提取技术研究

针对基于像元光谱特征提取沙化土地信息分类精度偏低的问题,以Landsat\|5 TM为数据源,基于面向对象的方法对沙化土地遥感信息提取技术进行研究。首先采用多尺度分割法对影像进行分割以获得同质区域,然后结合野外调查数据制成不同地物类型的多种特征图,从而确定提取目标地物的特征并建立沙化和非沙化地物提取决策树,最后对影像进行模糊分类,并对分类结果进行精度评价。结果表明,基于面向对象提取沙化土地信息的总精度达84.89%,Kappa系数为0.8077。研究结果为后续深入研究奠定了基础。     

基于地形信息的Landsat与Radarsat-2遥感数据协同分类研究

针对不同成像机理的光学与雷达遥感数据协同应用于地表信息提取瓶颈问题,提出了一种基于地形信息的光学与雷达数据协同分类方法。首先利用InSAR测量技术从Radarsat-2数据中提取DEM地形信息,然后构建基于地形信息的Landsat光学数据和Radarsat-2雷达数据的不同特征集输入模型,最后通过随机样本选取构建随机森林（Random Forest,RF）、支持向量机（Support Vector Machine, SVM）和决策树（Decision Tree,DT）分类算法模型提取地表信息。结果表明：①针对不同特征协同策略,在随机选取10%训练样本时,Radarsat-2干涉提取DEM与Landsat数据集提取精度优于ASTER GDEM与光学影像协同策略;②针对不同地表信息提取算法模型,通过50次随机选取训练样本构建模型评价分类精度,验证RF算法的鲁棒性和提取精度都要优于DT算法和SVM算法。研究充分利用光学和雷达遥感的优势信息,为光学和雷达遥感协同地表信息提取提供新的思路。     

基于地形信息的Landsat与Radarsat-2遥感数据协同分类研究

针对不同成像机理的光学与雷达遥感数据协同应用于地表信息提取瓶颈问题，提出了一种基于地形信息的光学与雷达数据协同分类方法。首先利用InSAR测量技术从Radarsat-2数据中提取DEM地形信息，然后构建基于地形信息的Landsat光学数据和Radarsat-2雷达数据的不同特征集输入模型，最后通过随机样本选取构建随机森林（Random Forest,RF）、支持向量机（Support Vector Machine, SVM）和决策树（Decision Tree,DT）分类算法模型提取地表信息。结果表明：①针对不同特征协同策略，在随机选取10%训练样本时，Radarsat-2干涉提取DEM与Landsat数据集提取精度优于ASTER GDEM与光学影像协同策略；②针对不同地表信息提取算法模型，通过50次随机选取训练样本构建模型评价分类精度，验证RF算法的鲁棒性和提取精度都要优于DT算法和SVM算法。研究充分利用光学和雷达遥感的优势信息，为光学和雷达遥感协同地表信息提取提供新的思路。     

基于深度学习的海岸线边缘检测网络模型

海岸线的动态监测对海岸带的规划管理具有非常重要的意义。由于海陆环境错综复杂,遥感影像中海陆边界光谱特征不明显,导致提取的海岸线定位不准确。提出一种融合语义分割网络和边缘检测网络的深度卷积神经网络模型(EWNet)。该模型包含2个分支流：语义分割流负责提取分层语义信息并用来指导边缘检测流获取岸线语义信息;边缘检测流通过语义分割流完善边缘语义信息。在“高分一号”遥感图像上的实验结果表明,与几种最新网络模型相比,EWNet获得了更精确的海岸线边界提取结果。     

Study on Mangrove Species Classification based on ZY-3 Image

A hybrid mangrove forest extraction and species classification model for large coastal region was proposed using a ZY-3 (ZiYuan-3) image in the eastern part of Beibu Gulf (located at the junction of Guangdong and Guangxi).Firstly,the coastline was extracted according to the spectral features of ZY-3 image.Secondly,the buffer zone along with the coastline was generated as the suitable area of mangrove distribution.Mangrove forests and non-mangrove areas were then further classified using threshold method based on object-based image classification in these areas.Finally,Mangrove forests were classified at specie level using three pixel-based supervised classification methods,k-Nearest Neighbor,Bayes,and Random Forest.The classification results and accuracies were also compared and discussed.The results indicated that object-based threshold method can extract the coastline accurately and map the mangrove forests effectively.The pixel-based random forest classifier can obtain satisfactory results (the overall accuracy of 82.24%) of mangrove species classification than the other classifiers.In summary,the hybrid mode proposed in this paper is suitable for mangrove forests mapping and species classification in a large region.It is also validated the feasibility application of ZY-3 image in coastal mangrove research.     

基于特征向量分解与散射机制判别指数的全极化SAR图像地物提取与分类

基于特征向量分解和基于散射模型的极化目标分解是全极化SAR非相干分解中的典型算法。本文对比研究了两种算法的特点及分解结果在地物识别分类方面的优势,在基于特征向量分解得到的H-Alpha特征平面的基础之上,引入散射机制判别指数来刻画地物的类别差异,从而能约束H-Alpha平面分割的界限以提高分类的精度,而且利用散射机制占优性强弱可辅助分类结果的解译。实验选取了鄱阳湖地区一景Radarsat-2标准全极化数据,实验结果对比表明一种散射机制占主导的地物,分类精度得到改善,特别是水域、形成二面角的目标区和成片分布的植被区域可以显著地提取出来。     

Mapping changes in coastline geomorphic features using Landsat TM and ETM+ imagery: examples in southeastern Brazil

Multitemporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery was used to assess coastline morphological changes in southeastern Brazil. A spectral linear mixing approach (SLMA) was used to estimate fraction imagery representing amounts of vegetation, clean water (a proxy for shade) and soil. Fraction abundances were related to erosive and depositional features. Shoreline, sandy banks (including emerged and submerged banks) and sand spits were highlighted mainly by clean water and soil fraction imagery. To evaluate changes in the coastline geomorphic features, the fraction imagery generated for each data set was classified in a contextual approach using a segmentation technique and ISOSEG, an unsupervised classification. Evaluation of the classifications was performed visually and by an error matrix relating ground-truth data to classification results. Comparison of the classification results revealed an intense transformation in the coastline, and that erosive and depositional features are extremely dynamic and subject to change in short periods of time.     

Improving forest type classification using the vegetation local difference index

A spatial feature extraction method was applied to increase the accuracy of land-cover classification of forest type information extraction. Traditional spatial feature extraction applications use high-resolution images. However, improving the classification accuracy is difficult when using medium-resolution images, such as a 30 m resolution Enhanced Thematic Mapper Plus (ETM+) image. In this study, we demonstrated a novel method that used the vegetation local difference index (VLDI) derived from the normalized difference vegetation index (NDVI), which were calculated based on the topographically corrected ETM+ image, to delineate spatial features. A simple maximum likelihood classifier and two different ways to use spatial information were introduced in this study as the frameworks to incorporate both spectral and spatial information for analysis. The results of the experiments, where Landsat ETM+ and digital elevation model (DEM) images, together with ground truth data acquired in the study area were used, show that combining the spatial information extracted from medium-resolution images and spectral information improved both classification accuracy and visual qualities. Moreover, the use of spatial information extracted through the proposed method greatly improved the classification performance of particular forest types, such as sparse woodlands.     

基于面向对象技术的鄱阳湖湿地地物分类研究

使用RadarsatＧ２全极化雷达数据,基于面向对象技术对图像进行分割,分析各类地物的散射特征,并对分割对象进行特征提取,依此设计各类地物的提取因子并对鄱阳湖湿地进行地物信息提取,最后利用实地采样数据和同期的高分一号影像数据对分类结果进行验证.研究表明:采用面向对象技术并结合地物散射特征设计的提取因子,可以有效克服雷达图像分类中常见的斑点现象,提高湿地分类精度.     

Classification of the Yellow River delta area using fully polarimetric SAR measurements

In this study, an analysis of the polarimetric synthetic aperture radar (SAR) capabilities to classify coastal areas is undertaken. The Yellow River delta (China) is selected as the test case since it represents an extraordinary environmental and economical area, which is characterized by a very heterogeneous scattering scenario, as witnessed by official reference data, provided by the Chinese government, that classified 12 different kinds of environment. Experimental results, obtained applying two well-known unsupervised classifiers, namely the H/α-based and the Freeman–Durden model-based algorithms, to a fully polarimetric SAR scene collected by Radarsat-2 in 2008 are compared and critically discussed. Both provide a satisfactory global accuracy (larger than 60% in average) with reference to the inland Yellow River delta area, but there are subareas that result in misclassifications and severe classification ambiguities. This study also suggests including single-polarization intensity information to improve the classification accuracy and to partly solve ambiguities.     

应用高分辨率遥感影像提取作物种植面积

利用中低分辨率遥感影像提取作物分类种植面积的精度,往往难以满足农业遥感估产的需要。随着新型传感器的不断出现,应用高分辨率遥感影像高精度地提取作物分类面积日益成为发展趋势。由于高分辨率遥感影像提供的地物纹理、色调与形状等信息更加丰富,当前基于对象的地物识别分类方法仍不成熟,处理操作中人为干预过多,而且较为复杂,因此尝试以地面调查信息为辅助参量,采用常规基于像元的最大似然法监督分类方法,依据多尺度遥感影像信息提取的原理,分阶段地逐步提取作物种植面积,以此为农业遥感估产服务。     

LBP特征分类的极化SAR图像机场跑道检测

目的 在极化合成孔径雷达（synthetic aperture radar，SAR）图像中常用直线检测进行机场跑道的识别，但是河流、道路等与机场跑道具有相似直线的地物容易对检测结果造成干扰，出现检测目标难定位、目标模糊、多虚警等问题。为此，本文设计了一种利用目标散射特性结合局部二值模式（local binary patterns，LBP）特征分类的极化SAR图像机场跑道区域检测方法，采用LBP特征对极化SAR图像进行有监督的分类来提取真实的机场区域。方法 首先利用异化散射功率对极化SAR图像进行阈值分割，然后通过形态学处理得到疑似机场跑道区域，同时构建机场跑道和非机场跑道两类训练样本，并提取、统计样本的LBP特征，形成直方图，得到特征向量训练支持向量机（support vector machine，SVM）二分类器，其中SVM二分类器采用了径向基函数（radial basis function，RBF）核函数；接着对疑似机场跑道区域构建LBP特征，送入SVM二分类器中分类，对机场跑道进行检测识别，最终得到真实的机场跑道区域。结果 利用UAVSAR（uninhabited aerial vehicle synthetic aperture radar）系统采集的7幅极化SAR图像数据进行实验检测，并选取基于几何特征辨识跑道的两种算法进行对比，3种方法均有效检测出了7幅场景中的真实跑道，但是本文方法在7幅数据中总的虚警和漏警个数均为1，而两种对比算法中的虚警个数分别为2和11、漏警个数分别为8和1。结论 本文方法不仅能有效检测出机场跑道区域，且检测效果更好，计算量较小，虚警和漏警率低，效率更高。     

结合双视觉全卷积网络的遥感影像地物提取

目的 遥感影像地物提取是遥感领域的研究热点。由于背景和地物类型复杂多样,单纯利用传统方法很难对地物类别进行准确区分和判断,因而常常造成误提取和漏提取。目前基于卷积神经网络CNN（convolutional neural network）的方法进行地物提取的效果普遍优于传统方法,但需要大量的时间进行训练,甚至可能出现收敛慢或网络不收敛的情况。为此,基于多视觉信息特征的互补原理,提出了一种双视觉全卷积网络结构。方法 该网络利用VGG（visual geometry group）16和AlexNet分别提取局部和全局视觉特征,并经过融合网络对两种特征进行处理,以充分利用其包含的互补信息。同时,将局部特征提取网络作为主网络,减少计算复杂度,将全局特征提取网络作为辅助网络,提高预测置信度,加快收敛,减少训练时间。结果 选取公开的建筑物数据集和道路数据集进行实验,并与二分类性能优异的U-Net网络和轻量型Mnih网络进行对比。实验结果表明,本文提出的双视觉全卷积网络的平均收敛时间仅为U-Net网络的15.46%;提取精度与U-Net相当,远高于Mnih;在95%的置信水平上,该网络的置信区间明显优于U-Net。结论 本文提出的双视觉全卷积网络,融合了影像中地物的局部细节特征和全局特征,能保持较高的提取精度和置信度,且更易训练和收敛,为后续遥感影像地物提取与神经网络的设计提供了参考方向。     

一种新的基于Wishart MRF的全极化SAR图像分类方法

无监督的Wishart分类算法在多次迭代后,容易出现错分现象,即多个类别属于同一类散射机制,或者多种散射都拥有相同的类别标签。针对此问题,提出了一种新的基于Wishart MRF的无监督全极化SAR图像分类方法。新方法改进了散射机制保持的方式,即并不是完全限制像素点的散射机制,而是根据像素点的散射机制在迭代过程中给定一个有限的范围。同时,使用一种自适应区域的MRF方法来提取像素点的先验信息。该方法不仅考虑了全极化SAR数据的散射性质,而且结合了统计特性和邻域信息,并在一定程度上保持了散射性质。实验结果证明,与传统的Wishart和基于散射机制保持的Wishart算法相比,该方法在JPL/NASA的AIRSAR数据上取得了更好的分类结果。     

基于多尺度的肿瘤轮廓结构不规则性特征分析

皮肤肿瘤轮廓的结构不规则性特征提取在计算机辅助诊断黑色素瘤中具有重要意义。在前期轮廓不规则性的局部分形维基础上,提出基于多尺度曲率的轮廓不规则特征提取方法,采用相邻尺度间特征差异度量来增强大尺度下的甄别良恶性皮肤肿瘤的能力。通过局部分形维和曲率分析比较表明,相邻尺度间特征差异度量方法具有类间Hausdorff距离随尺度增大的特性,但局部分形维较曲率分析具有较大的Hausdorff距离值。实验结果表明,上述方法不仅具有较强的结构不规则性的分类能力,并且有助于削弱纹理不规则性对分类结果的影响。     

Ground object information extraction from hyperspectral remote sensing images using deep learning algorithm

Mining and utilizing coal resources play an influential role in economic development. In this regard, the feature information extraction in the area is researched to accurately and efficiently assist the production arrangement and deployment in the mining area. First, the detection ability of Hyperspectral Remote Sensing Image (HRSI) technology is analyzed. It has high spectral resolution and many bands. Specific bands can be extracted as needed to highlight target features. According to the characteristics of HRSIs, the data spectrum information and spatial information are comprehensively utilized, and the Convolutional Neural Network (CNN) based on deep learning is employed for feature extraction. CNN allows the machine to automatically obtain data features by learning and guide the classification of features. Taking the Liuyuan research area in Gansu as an example, three CNN models are used to extract and classify the ground features in the area. The VGG-19 model can provide the highest classification accuracy rate, reaching 87.3%; the VGG-16 model has the highest classification accuracy rate of the ground in the mining area, reaching 95.2%. ResNet model has the best effect on road classification. Then, the lithology classification is applied based on Thermal Airborne Hyperspectral Imager (TASI) data. The noise level of the first 20 bands is comparatively stable; afterward, it increases exponentially, showing a higher noise level, and the spectrum curve of the data after denoising becomes smoother. The end-member extraction method is employed to extract 25 end-member spectra of almost all lithology in the research area from the image. The similarity coefficient clustering analysis is employed to group the curves, which are divided into six categories in total. The separability of similar categories can be constrained by the objective function using the dictionary learning method, and the accuracy of the sparse representation of the category spectrum can be improved. The spectral matching method is used to subdivide each group of mapping results, suggesting that in the research area, granite is the most widely distributed, followed by diorite, andesite, and quartzite. Deep learning algorithms are applied to extract ground feature information, which is of great significance to the safety production in the mining area. The hyperspectral remote sensing rock and mineral thematic information extraction module is developed, which preliminarily realizes the quantitative acquisition and high-precision identification of typical mineral information, and provides technical support for the research of remote sensing geological evaluation technology of resource exploration in the new era.     

高寒干旱地区环境遥感信息提取研究

根据高寒干旱地区环境研究的特点,研究耻适合于遥感信息提取的区域环境综合分类系统,提出了利用现有成熟的图像分类和专题信息提取的技术方法,在ＧＩＳ的支持下实现了多种遥感信息与非遥感信息的复方。利用多重判据在ＧＩＳ环境下,通过基于知识的知识和修改,对初分类结果中相混淆异类进行了有效分离,达到类型纯化。