基于纹理信息CART决策树的林芝县森林植被面向对象分类

以西藏自治区林芝县的Landsat-8影像、地形图为信息源,结合样地调查数据及森林资源二类调查数据,研究基于纹理信息的CART决策树的面向对象分类对研究区内的森林地物类别进行提取,分类的总体精度和Kappa系数分别为82.53%和0.768,相较于不利用纹理信息的决策树分类和基于最大似然分类法的研究区地物类别的提取总体精度均高近10%,Kappa系数分别高0.12和0.111。结果表明:基于纹理信息的CART决策树面向对象分类方法对研究区Landsat-8影像进行植被类型提取,分类结果较好,能够满足研究要求。     

基于GF-1影像的冬小麦和油菜种植信息提取

高分(GF)系列卫星的相继发射为国产高分辨率遥感数据的应用创造了新的机遇。为探索GF数据在中小尺度农作物遥感监测领域中的可行性和建立相适应的技术体系,以扬州市为例,运用决策树模型和面向对象分类方法,研究GF-1卫星的宽视场(wide field of view,WFV)数据在农作物种植信息提取中的可行性,并探索提高其提取精度的处理方法。结果表明:分区处理可以降低作物空间分布对种植区提取的不利影响;冬小麦总体精度为97%,Kappa系数为0.93;油菜总体精度为96%,Kappa系数为0.84。综上所述,国产GF-1 WFV影像可以应用于农作物种植信息的提取,并为粮区农作物种植空间调整和优化管理提供重要参考和决策支持。     

结合地籍数据的高密度城区面向对象遥感分类 

利用高分辨率遥感影像和GIS辅助数据,对高密度城区进行面向对象的土地利用覆被分类研究。使用NAIP高分辨率航空遥感影像,在多尺度影像分割的基础上,针对特定地物选择合适的影像分割参数。采用决策树方法建立高密度城市地区的分类规则,并结合该地区地籍图数据作为辅助数据,逐步进行高密度城市地区地物信息提取。利用辅助数据进行面向对象的遥感分类效果优于单纯依靠遥感影像进行的分类,且有效提取了道路和复杂的房屋等信息,得到了理想的分类结果,其总分类精度从常规面向对象方法的84.08%提高到89.79%。利用辅助数据进行遥感分类提高了高分辨率遥感影像的分类精度,说明了利用辅助数据进行遥感分类方法的有效性。     

基于决策树方法的淡水沼泽湿地信息提取——以三江平原东北部为例

湿地是生态系统的重要组成部分,及时、准确地获得湿地基础信息,对湿地的动态监测、保护与可持续利用及其它领域的研究具有重要意义。以三江平原东北部沼泽湿地为例,利用分类回归树(Classification and Regression Tree,CART)算法从训练样本数据集中挖掘分类规则,集成遥感影像的光谱特征、纹理特征和地学辅助数据建立研究区湿地信息提取的决策树模型。用实测的GPS样本点对分类结果进行精度验证,并与最大似然监督分类方法(Maximum Likelihood Classification,MLC)进行对比。结果表明,基于CART的决策树分类结果的总精度和Kappa系数分别为82.65%和0.7935,分类精度较MLC监督分类方法有明显提高,是内陆淡水沼泽湿地信息提取的有效手段。     

基于面向对象分类的南方水稻种植面积提取方法

南方丘陵地区水稻种植具有分散、地块小、形状多样等特点,利用中低分辨率遥感数据提取水稻种植面积,难以满足精度要求。以SPOT5遥感影像为数据源,应用面向对象的分类方法提取了广西玉林市辖区晚稻种植面积。针对试验区不同稻作区的种植特点,选择其适合的尺度及参数进行多尺度影像分割,建立影像对象的层次结构,计算对象的光谱、几何及拓扑关系等特征,形成分类规则对不同稻作区进行信息提取。采用野外实地调查数据对分类结果进行类别和面积一致性检验,总体精度96.31%,Kappa系数0.9226,面积一致性精度99.92%。
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物候特征辅助下的随机森林宏观尺度土地覆盖分类方法研究

通过遥感技术获取大范围土地覆盖信息对于监测、理解和预测自然资源具有重要的科学意义.MODIS数据是当今宏观尺度土地覆盖研究的主要数据源.本文以河北省为研究区,应用MOD13Q1数据产品,构建MODIS NDVI时间序列,从中反演物候特征作为参与分类的主要辅助信息,并采用随机森林分类方法进行宏观尺度土地覆被分类实验,并与单决策树(CART)进行对比分析.实验结果表明,物候特征辅助下的随机森林宏观尺度土地覆被分类方法的总体精度为87.2％,Kappa系数为0.83,比CART单一决策树精度提高了17.9％;应用物候特征参与分类,使得总体精度提高2.6％;其中,旱地和建筑用地精度分别提高了6.7％和11.9％.     

基于CART决策树分类的沙漠化信息提取方法研究

沙漠化是我国北方土地退化的主要形式之一,也是国内外研究中的重要环境问题。以民勤县为例,讨论了CART(Classification and Regression Tree)决策树在沙漠化研究中的应用,并使用Landsat8OLI遥感影像为数据源,构建了一种可行的用于研究区的沙漠化信息提取规则,进行地表沙漠化信息提取。结果表明:所构建的决策树模型结构简单,沙漠化提取效果较好;在研究区域达到87.70%的分类精度,Kappa系数为0.848 4,分类精度也较高。同时,归一化裸露指数(NDBI)和地表反照率(Albedo)是两个明显的沙漠化特征量,在沙漠化提取中起着重要作用。然而,CART决策树作为一种基于监督的分类方法,模型构建时,选择相对较高质量的训练样本和准确合理的输入端变量,可大大提高沙漠化信息的提取精度。     

面向对象的高分辨率影像分类与信息提取

采用面向对象遥感影像分类方法对高分辨率遥感影像进行了信息提取实验,并将其与基于像元方法的信息提取结果进行了对比分析。实验研究表明,在目视效果上,传统方法的分类结果图中“椒盐现象”非常明显,而面向对象方法可以有效地避免“椒盐现象”;在分类精度上,面向对象方法分类结果的总体精度、Kappa系数、生产者精度、用户精度、Hellden精度和Short精度均明显高于传统方法,各类地物提取效果显著提高,总分类精度提高21.76%,Kappa系数提高0.2756。面向对象方法在高分辨率遥感影像信息提取中具有明显的优势。     

基于面向对象信息提取技术的城市用地分类

针对高分辨率遥感影像的城市用地分类,引入了面向对象的信息提取技术,并将其与传统基于像素光谱信息的分类方法进行了比较。在此基础上详述了面向对象信息提取的关键技术---多尺度影像分割和基于分割的分类技术。以城市作为研究区,实现城市用地的自动分类。图像处理过程包括几何校正、HIS融合、图像分割和图像分类。最终分类结果表明:视觉上,面向对象信息提取技术克服了传统方法无法克服的“椒盐”噪声的影响;精度上,面向对象信息提取技术的总体精度高达84.82%,比最大似然法的总体精度提高了10.95%,并且各类地物信息的提取精度均有所提高,其中草地、道路、建筑物阴影的精度较高。     

基于高分辨率影像数据的耕地信息提取应用研究

遥感影像地物信息丰富,使得直接通过影像获取耕地地块信息成为可能。本文介绍了以高分辨率遥感影像数据为基础图件,应用ERDAS、ARCGIS、eCogntion软件平台,通过非监督分类法、监督分类法和面向对象分类法分别对试验区域提取耕地信息的过程。试验结果表明,遥感技术是获取耕地信息提取的有效途径之一,监督分类进度为75．5％,面向对象法精度为90％。面向对象分类方法提取精度较高,效果最好。     

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。     

Study on Extraction Method of Abandoned Farmland based on the Seasonal Variation Characteristics of Remotely Sensed Images

In southwestern China, the cultivation conditions are poor, the plots are relatively fragmented, and the types of plots are complex. Therefore, the use of low and medium resolution remote sensing data is not able to satisfy the needs of abandoned farmland extraction. This paper explored the ability of single or multi-phased high resolution remotely sensed images in detecting abandoned farmland in southwest China, using Xiuwen County, Guizhou Province, China as a case study area. Remote sensing based monitoring methods for abandoned farmland were developed, providing a reference for the statistical survey of abandoned farmland in southwest China.The extraction method of abandoned farmland was proposed based on the field survey data, considering different types of abandoned farmland. Sensitive feature sets of different types of abandoned farmland were identified from a series of features including the spectral characteristics, vegetation indices and multi-temporal difference vegetation indices. The CART decision tree classification method was applied on the selected sensitive features to extract abandoned farmland. The results showed that:(1) There was a significant difference in the recognition ability of single-phase image in extracting different types of abandoned farmland, so it was difficult to use only single-phase image to extract abandoned farmland with high accuracy; (2) The vegetation index change characteristics of different time phases had strong recognition ability for abandoned farmland, and the ratio vegetation index was better than the difference vegetation index and normalized vegetation index; (3) The spatial distribution map of abandoned farmland and the statistical analysis of abandoned farmland area were carried out in Xiuwen County, Guizhou Province. The area of abandoned farmland in Xiuwen County was about 6,460 hectares, accounting for 13% of the cultivated land area.（4）Based on multi-temporal high-resolution remote sensing data, the method of detecting abandoned farmland using seasonal variation characteristics can meet the requirements of high-precision extraction of abandoned farmland in southwest China, and the results provided technical reference for remote sensing survey and mapping of abandoned farmland in large-scale.     

基于季相变化特征的撂荒地遥感提取方法研究

在我国西南地区耕种条件差,地块比较破碎,地块类型比较复杂,中低分辨率遥感数据难以满足撂荒地提取的需要。选取贵州修文县为试验区,基于高分辨率卫星遥感数据（哨兵2号）,探索单期或多期影像在中国西南地区的撂荒地检测能力,构建撂荒地遥感监测方法,为今后我国西南地区撂荒地统计调查提供参考。结合野外调查数据,在划分不同撂荒地类型基础上,综合遥感影像的光谱特征、植被指数特征以及多时相植被指数变化特征分析,优选不同类别撂荒地遥感提取敏感特征集,利用CART决策树分类方法,提取不同类型的撂荒地。结果表明：①单个时相对不同类型的撂荒地识别能力差异显著,基于单时相影像,难以开展撂荒地高精度遥感监测提取;②不同时相的植被指数变化特征对撂荒地的识别能力较强,其中比值植被指数优于差值植被指数和归一化植被指数;③以贵州修文县为例,开展了撂荒地空间分布制图及撂荒面积统计分析,修文县撂荒地面积约为6 460 hm²,占修文县耕地面积的13%;④基于多时相高分辨遥感数据,通过季相变化特征构建的撂荒地检测方法,能够满足我国西南地区撂荒地高精度遥感监测提取,为大范围撂荒地遥感调查和制图提供技术参考。     

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.     

Comparison of Machine Learning Methods for Land Use/Land Cover Classification in the Complicated Terrain Regions

Aiming at the characteristics of varied and complex geomorphic types,crisscross network of ravines and broken terrain in high altitude complicated terrain regions,it is very important to study and find the rapid and effective land use/land cover classification method for obtaining and timely updating of land use information.Taking the Huangshui river basin located in the transitional zone between the Loess Plateau and the Qinghai-Tibet Plateau as acasestudy area,the objective of this study is to explore a kind of effective information extraction method from comparison of four kinds machine learning methods for complicated terrain regions.based on Landsat 8 OLI satellite data,DEM and combined with various thematic features,on the basis of geographical division of the study area,artificial neural network,decision tree,support vector machine and random forest four machine learning methods for land use information extraction were used to obtain land use data,and confusion matrix was constructed to evaluate classification accuracy.The results showed that the classification accuracies of random forest and decision tree are obviously higher than those of support vector machine and artificial neural network.The random forest method has the highest classification accuracy,the overall classification accuracy is 85.65%,the Kappa coefficient is 0.84.based on the above classification,Random forest classification method was chose to further classify Landsat 8 fusion datafrom panchromatic 15 meter and multispectral 30 meter image,the overall classification accuracy is 86.49% and the Kappa coefficient is 0.85.This indicated that the random forest classification method can obtain higher classification efficiency while ensuring the classification accuracy.It is very effective for the extraction of land use information in complicated terrain regions.Data fusion can improve the classification accuracy to a certain extent.     

一种面向对象结合变差函数的高分辨率遥感影像茶种植区自动提取方法

针对传统的茶区提取主要依靠人工野外勘测方法,需要耗费大量的人力物力,时效性差,精度低,不能及时有效地获取茶区空间分布信息,同时茶区在光谱特征上与林地、梯田等具有极强的相似性,茶种植区的遥感识别工作难度高等问题,提出了一种面向对象结合变差函数的茶种植区自动提取方法.为了解与动态监测茶的种植情况,选取位于贵州省铜仁市的4块...     

基于多时相协同变化检测的耕地撂荒遥感监测

针对地表覆被复杂、地块破碎等原因导致的撂荒地提取精度较低问题,提出一种基于多时相协同变化检测的耕地撂荒信息提取方法。以河北省石家庄市鹿泉区为研究区,采用Sentinel-2A和Landsat 7多光谱影像,在野外样本的支持下,分析耕地各种覆盖类型的归一化植被指数（Normalized Difference Vegetation Index,NDVI）季相变化规律,以季节性撂荒、常年性撂荒、冬小麦、多年生园地为分类体系,构建多时相协同变化检测模型,开展研究区耕地撂荒状态遥感监测。研究结果表明：基于Sentinel-2A影像的季节性撂荒和常年撂荒耕地的分类精度分别为95.83%和96.55%;基于Landsat 7影像的季节性撂荒和常年撂荒耕地的分类精度分别为91.67%和93.10%;2019年鹿泉区季节性撂荒占耕地面积的4.7%,常年撂荒耕地占7.1%。利用该方法能够快速、准确地获取研究区耕地空间分布、面积等信息,对于不同分辨率的影像均具有较好的撂荒地提取精度。     

Classification of Remote Sensing Image based on Object-oriented Method:A Case Study of Baixiang County

Remote sensing is the main means of extracting land cover types,which has important significance for monitoring land use change and developing national policies.Object-based classification methods can provide higher accuracy data than pixel-based methods by using spectral,shape and texture information.In this study,we choose GF-1 satellite’s imagery and proposed a method which can automatically calculate the optimal segmentation scale.The object-based methods for classifying four typical land cover types are compared using multi-scale segmentation and three supervised machine learning algorithms.The relationship between the accuracy of classification results and the training sample proportion is analyzed and the result shows that object-based methods can achieve higher classification results in the case of small training sample ratio,overall accuracies are higher than 94%.Overall,the classification accuracy of support vector machine is higher than that of neural network and decision tree during the process of object-oriented classification.     

湖北省兴山县10 年土地利用/覆盖变化遥感监测分析

利用多平台遥感影像和地理辅助信息获取土地利用/覆盖信息是遥感应用的一个重要方面。选择三峡库区湖北省兴山县作为研究区域, 以两期TM 和SPOT 影像为土地利用/覆盖信息提取的遥感信息源, 并在分类过程中结合该地区的地理辅助信息。通过对分类结果分析, 林地和耕地是兴山县两大用地类型, 运用数据运算规则, 获取土地利用/覆盖动态转换矩阵, 对兴山县10 年间土地利用/覆盖变化趋势及引起土地利用/覆盖模式变化的原因进行分析和评价。