EN中文

Journal of Geo-information Science >

2016 , Vol. 18 >Issue 7: 962 - 968

DOI: https://doi.org/10.3724/SP.J.1047.2016.00962

Orginal Article

The Application of High-resolution Remote Sensing and Change Detection Technologies in Law Enforcement and Supervision of Land Resources

  • WU Tianjun , 1, 2, * ,
  • XIA Liegang 3 ,
  • WU Wei 3 ,
  • MA Jianghong 1
Expand
  • 1. Department of Mathematics and Information Science, College of Science, Chang′an University, Xi′an 710064, China
  • 2. Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhoushan 316022, China
  • 3. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310026, China
*Corresponding author: WU Tianjun, E-mail:

Received date: 2015-12-20

  Request revised date: 2016-01-24

  Online published: 2016-07-15

Copyright

《地球信息科学学报》编辑部 所有
Fold

Abstract

Law enforcement and supervision of land resources are important components in the land resources management systems. Their current way of working is mainly manipulated by the traditional manual operation, which is not efficient in performance. With the launch of high-spatial-resolution remote sensing satellites, the application of details contained within these received images makes it possible to accurately and rapidly enforce the law and supervise the land resources with the adoption of remote sensing technology. Therefore, in order to analyze the actual requirements of the applications and the latest technological developments, this paper intends to extract the illegal construction land information using the high-spatial-resolution remote sensing images, and we choose the Huangyan district to be the demonstration area to carry out the county-level law enforcement and to implement proper supervision to land resources. We have extracted the suspected illegal build-up construction sites using the change detection and urban extraction technologies. The experiment achieves a good result and demonstrates the significant potentials of domestic high-spatial-resolution satellite in the practical applications of law enforcement and supervision of land resources.

Key words： high-spatial-resolution remote sensing; law enforcement and supervision of land resources; change detection; illegal build-up construction land; domestic satellite

Cite this article

WU Tianjun , XIA Liegang , WU Wei , MA Jianghong . The Application of High-resolution Remote Sensing and Change Detection Technologies in Law Enforcement and Supervision of Land Resources[J]. Journal of Geo-information Science, 2016 , 18(7) : 962 -968 . DOI: 10.3724/SP.J.1047.2016.00962

1 引言

土地资源是人类生存与发展的基础,土地类型的变化对区域土地资源及其安全性有决定性的作用[1]。受人类活动支配的土地利用导致的土地覆盖格局的改变,会引起土地生态系统的一系列变化（如生物多样性的改变等）,甚至对全球气候都会产生决定性的影响[2]。近些年,中国土地相关的问题日益突出,威胁着中国土地资源的安全和可持续发展。不合理的土地利用对当前生态环境、耕地保有量造成了严重威胁。因此,加大土地利用的监察力度,及时发现并快速查处土地利用的违法违规行为,有助于规范土地管理、切实保护耕地。目前,土地执法监察业务已成为当前全国各级国土部门的业务重点之一。
与其他监测手段相比,遥感监测具有速度快、精度高、范围广等特点,能为国土资源管理工作提供基于事实影像的、可精确测量的以及可作为基础信息的土地利用动态监测结果。特别是随着越来越多的高空间分辨率（简称“高分辨率”）遥感卫星的发射,逐渐形成了高分卫星遥感及其应用的新时代[3],为国土资源监察提供了丰富数据源。如QuickBird、IKONOS、GeoEye-1、Pleiades-1、WorldView-3等卫星数据均已到达了亚米级的空间分辨率。随着国家高分辨率遥感专项的稳步推进与实施,中国国产卫星的空间分辨率和质量都得到了极大提高,仅以民用光学卫星为例,目前在轨且应用较广泛的有资源1号-02C（ZY-1-02C）、资源3号（ZY3）、高分1号（GF1）、高分2号（GF2）等。这些国产卫星接收的影像数据无论从“质”上还是“量”上都有了很大提升,能在国土应用领域发挥重要的作用[4],但同时也要求我们针对国产卫星数据自身特点和应用需求发展适用的算法。
本文监察的目标是非法新增建设用地,是指实地已新增建设的非法用地,即最终输出信息应是变化后的土地利用类型为建设用地的未规划区域。而利用高分辨率遥感技术实施变化检测能有效地提高执法监测工作水平,并快速地完成新增建设用地的发现和违法查处。目前,针对新增建设用地的遥感检测主要有2个技术思路：（1）直接依据影像中建设用地的纹理、形状、阴影关系等空间结构特征提取建设用地变化信息,例如,苏俊英等利用灰度方差纹理特征提取了高分辨率影像居民地信息[5];袁修孝等采用光谱和纹理特征进行高分遥感的建设用地变化检测[6];季顺平等运用建筑物阴影分析了建筑物的变化[7];苏娟等结合纹理和边缘特征提取了高分影像中的人工目标信息[8];Bouziani等利用先验规则进行了高分辨率遥感的建设用地变化信息提取[9];（2）在确定变化区域和变化转移类型基础上,筛选出建设用地变化信息,例如,方针等确定变化区域后在目标靶区筛选出了建设用地的变化信息[10];耿忠等在利用变化检测算法获取变化区域基础上,进一步利用特征相似度匹配分析筛选出了感兴趣的建设用地[11]。
鉴于上述研究现状和当前应用需求分析,本文以国产高分辨率影像为主要数据源,以浙江省台州市黄岩区为应用示范区,开展基于高分辨率遥感影像及其变化检测技术的违法新增建设用地提取试验,生产出疑似违法新增建设用地图斑成果,以便监管部门掌握相关信息并开展后续的整治工作,控制违法用地比例。全文以应用为目标导向,力图为新型城镇化背景下国产高分辨率卫星数据在县区级城镇土地执法监察应用的顺利开展提供初步尝试。

2 研究区及数据源

本文选择浙江省台州市黄岩区作为实验区,总面积约1000 km2。该地区近几十年来发展迅速,特别是辖区东部的城市化水平较高,已成为台州市人口、资源、环境与社会经济发展矛盾较为突出的地区之一。随着城镇化进程的推进,该地区的土地类型变化问题更具特色,尤其是城乡结合部的生态安全面临着土地利用变化带来的严重威胁。这些特点使黄岩区成为理想的建设用地变化检测研究 区域。
作为黄岩国土资源管理业务体系的重要内容,土地执法监察工作目前仍采用工作效率不高、规范化与程序化水平较低、随意性较大的传统手工作业方式来完成,已难适应新形势下的执法监察要求。因此,在调研了当地国土局的日常巡查、卫片执法、违法案件处理等业务流程后,拟结合当地需求引入高分辨率遥感监测手段,开展违法新增建设用地监测应用,旨在通过多期高分辨率影像数据的变化检测提取疑似违法的新增建设用地图斑,服务当地土地执法监察的业务实施。
针对需求,本文收集了1期2013年年底的WorldView-2（WV2）影像（空间分辨率为0.5 m）（图1（a））,以及1期2014年覆盖黄岩区的ZY3卫星融合影像（空间分辨率为2 m）（图1（b））,形成了对黄岩区主城区与城乡结合部等关键区域的多期覆盖监测（图1）。其中,图1（b）的影像成像时间为2014年5月1日(左)与2014年9月1日(右),其按照图中绿色实线进行有效数据的裁剪与拼接。由于2期影像分辨率存在差异,在变化检测之前需先将0.5 m分辨率的WorldView-2数据重采样至2 m分辨率,以保持影像尺度的一致性;同时,针对2期影像的色彩差异,进行了色彩归一化处理。在辅助数据方面,收集和整理了黄岩区的2012年的土地利用矢量图（图2）以及2009-2013年的历年农转用矢量数据（涵盖了通过规划审批、合法可建的地块图斑区域）。为了使收集的各类数据在地理位置上相互匹配,对上述多源数据进行了几何与正射校正、矢量与栅格、栅格与栅格配准等预处理。
Fig. 1 Two images used for monitoring the illegal build-up construction land

图1 违法新增建设用地监测应用的2期影像数据

Fig. 2 Land use data set of Huangyan district in 2012

图2 2012年黄岩区土地利用数据

3 非法新增建设用地提取方法

为快速地发现地表变化信息,首先使用不同时期的高分辨率遥感影像进行变化检测。针对高分辨率遥感影像地物细节丰富、光谱易混淆的情况,引入图像分割算法提取同质对象并以此作为最小检测单元,进而依据对象光谱与纹理特征来判断变化区域。面向对象的变化检测算法流程如图3所示,主要涉及影像分割、特征提取、相似度计算、多特征相似度证据融合以及二值化等环节。
Fig. 3 Flow chart of the object-based remote sensing change detection method

图3 面向对象的遥感变化检测方法流程

（1）将2个时相数据的组合影像（6波段）进行均值漂移分割,获得第1、2时相空间位置匹配的 对象;
（2）分别提取出2个时相对象的光谱、纹理特征,鉴于光谱和纹理特征对于建设用地的稳定性及其新增变化的敏感性,本文在光谱特征方面计算了2个不同时相对象的3波段的亮度,而在纹理特征方面计算了2个时相对象内各像元灰度空间相关特性的测度（即灰度共生矩阵（GLCM）的中值、协方差、同质性/逆差距、反差、差异性、熵、二阶距、自相关等）均值;
(3)基于欧氏距离及其归一化分别计算2个时相对象上述特征的相似度;
(4)对2个时相对象的不同特征相似度进行DS证据融合[12],获得对象相似度专题层;
(5)设置适当的阈值（在大津法自适应计算出的阈值基础上增加了0.1）,二值化对象相似度专题层,得到变化区域分布。
通过上述算法获取的仅仅是2个时相对比发生变化的区域,但具体的变化类型不能确定为建设用地。为此,本文进一步设计了违法新增建设用地的完整提取流程,具体技术路线如图4所示。首先,通过上述面向对象的变化检测技术提取出变化图斑[13-17],并从第2时相影像中利用像元形状指数PSI[18]、建筑物存在指数PanTex[19-21]等构建建筑物的显著特征,筛选出PSI和PanTex特征均大于其均值的对象作为后时相影像上的建设用地疑似图斑;然后,与之前获取的变化区域进行叠加分析,提取同时满足“该区域发生变化”、“该区域在第2时相影像中的土地类型为建设用地”这2个条件的图斑作为疑似新增建设用地区域;最后,结合土地利用现状数据、规划部门的农转用矢量数据,筛选出最终的疑似违法新增建设用地,并交由执法人员实施室内核查、实地勘察以及违法处理。
图4是一个人机交互的应用实施过程。其中,利用计算机进行2期影像数据间的变化检测以及进一步的新增建设用地图斑自动提取必定存在一定的误差。而从应用需求出发,为了尽可能降低漏检率,本文在图2的变化检测步骤中设置了较大的二值化阈值参数,以尽可能避免目标靶区的遗漏;在此基础上,配合使用2012年土地利用矢量图以及2009-2013年的农转用矢量图进行虚警伪图斑的剔除;最后,结合人工目视检查和编辑等最终完成成果制图。也就是说,计算机自动检测生成的结果需要经过疑似图斑的室内排除以及人工后处理才能提供给业务部门,供其参考分析对应时间周期内的违法建设用地情况,并经实地验证后开展整治工作。
Fig. 4 Flow chart for extracting the illegal new construction land using high-resolution remote sensing images

图4 高分辨率遥感违法新增建设用地提取技术流程图

4 结果与讨论

在上述试验思路下,利用收集的数据集生成疑似违法新增建设用地图斑及中间文件如图5所示。本文提取了合法变化图斑和疑似违法图斑,图斑面积不小于400 m2,其中计算机自动提取图斑数共计5018个,合法变化图斑数852个,违法变化图斑数4166个（图5(a)）;人工目视检查剔除伪图斑后图斑数减少为671个,其中合法变化图斑数289个,违法变化图斑数382个（图5(b)）。
Fig. 5 Extraction result of the suspected illegal new construction land

图5 提取的疑似违法新增建设用地图斑结果

结合图5的细节观察以及图斑人工剔除后的数量变化可知,整个流程中计算机自动检测的图斑结果含有较高的虚警率。造成大量伪图斑的原因是,（1）尽管2期影像做了必要的预处理,但在配准精度、辐射一致性、阴影遮挡等方面仍存在一定误差,进而传播至变化检测算法中导致虚警;（2）为了减少漏检率,在变化检测算法中设置了较大的二值化阈值参数,造成了一定量的虚警。为此,人工目视检查的后处理过程就显得很有必要。
图6给出了检测结果的局部细节,其中绿色图斑为合法变化图斑,红色图斑为疑似违法图斑。通过比对成果细节与结合人工目视检查可发现,生成的新增建设用地矢量图斑成果能较好地反映黄岩区主城区及城乡结合部周边等关键区域在研究期内的新增建设用地情况,提取的图斑边界较精准,能与农转用等合法区域图斑实现区分。这些成果已得到黄岩区国土局业务人员的实地验证和认可,反馈的用户精度在65%以上（大部分错误图斑多是由于影像空间与辐射分辨率不够或实地地物遮挡所导致）。尽管室内检测的精度仍有很大的改进余地,但这已很大程度上改善了其过往开展土地执法监察的被动局面,体现了相关技术在业务部门中的实际应用价值。
Fig. 6 Details of the detection results

图6 检测结果细节

5 结论

本文针对土地执法监察的业务需求,以台州市黄岩区为示范区,开展了基于国产高分卫星的遥感监测应用,设计了满足应用需求的信息提取算法和应用流程,生产出疑似违法新增建设用地信息产品。通过研究,初步验证了国产高分辨率遥感影像及其变化检测技术在土地执法监察业务中的应用可行性,对国产高分辨率卫星影像在国土行业的大规模综合应用提供了较好的示范。
从方法实现上看,本文算法仍有进一步的改进余地,在结果验证上也应更加定量化,这些都需要在日后的工作中逐步解决和日益完善;从应用需求上来看,由于流程中分割算法的参与,目前的图斑成果锯齿现象较严重,后续可进一步考虑生产更易为业务部门接受的图斑产品,从而使国产高分辨率数据能全面高质量地服务于土地执法监察应用。

The authors have declared that no competing interests exist.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
刘纪远. 中国资源环境遥感宏观调查与动态研究[M].北京:中国科学技术出版社,1996.

[ Liu J Y.Macro survey and dynamic study on Chinese resources & environment using remote sensing[M]. Beijing: China Science & Technology Press, 1996. ]

[2]
美国国家科学院.理解正在变化的星球(刘毅,刘卫东译)[M].北京:科学出版社,2011.

[ United States National Academy of Sciences. Understanding the changing planet: strategic directions for the geographical sciences (translated by Liu Y, Liu W D)[M]. Beijing: Science Press, 2011. ]

[3]
周成虎,骆剑承.高分辨率卫星遥感影像地学计算[M].北京:科学出版社,2009.

[ Zhou C ., Luo J C.Geo-computing of high resolution satellite remote sensing images[M]. Beijing: Science Press, 2009. ]

[4]
宫鹏,黎夏,徐冰.高分辨率影像解译理论与应用方法中的一些研究问题[J].遥感学报,2006,10(1):1-5.近年来，不断发展的遥感技术使遥感数据呈现出高空间分辨率、高光谱分辨率和高时间采集频率的特点。卫星图像空间分辨率已经提高到0．6m级，而航空遥感数字影像分辨率高达0．1m以上。光谱分辨率高达3―4nm。不断发展的高分辨率遥感数据能够提高信息提取和监测精度，并拓展遥感数据的应用范围。目前，国外已经加快对高分辨率图像，特别是高空间分辨率影像，在城市环境、精准农业、交通及道路设施、林业测量、军事目标识别和灾害评估中的应用。但是总的情况是自动化程度不高。介绍高空间分辨率影像信息提取、高光谱和偏振影像信息提取、影像数据融合和高分辨率遥感变化探测等方面迫切需要研究的一些科学问题及其意义。建议建立图像知识库，改善数据共享环境，为有志于从事这方面研究的学者提供参考。

DOI

[ Gong ., Li ., Xu B.Interpretation theory and application method development for information extraction from high resolution remotely sensed data[J]. Journal of Remote Sensing, 2006,10(1):1-5. ]

[5]
苏俊英,曹辉,张剑清.高分辨率遥感影像上居民地半自动提取研究[J].武汉大学学报·信息科学版,2004,29(9):791-795.设计了一种基于3×3区域灰度方差纹理特征的高分辨率遥感影像居民地特征分析算法,通过高斯 模糊处理提高影像上居民地区域纹理特征一致性的同时,加大了其同背景地物纹理特征值的差异,并设计了相应的居民地自适应分割阈值求取算法,提出了针对居民 地与道路提取分离的骨架化分析算法.

DOI

[ Su J ., Cao ., Zhang J Q.Semi-automatic extraction technique of residential area in high resolution remote sensing image[J]. Geomatics and Information Science of Wuhan University, 2004,29(9):791-795. ]

[6]
袁修孝,宋妍..

[7]
季顺平,袁修孝.一种基于阴影检测的建筑物变化检测方法[J].遥感学报,2007,3(5):323-329.提出了一种基于背景模型的针对建筑物的阴影检测及变化检测方法. 传统的基于背景模型的目标检测算法认为影像局部区域的自然背景符合高斯正态分布,而含有人工目标的区域则不符合这种分布,从而将目标区与自然地物区区分开 来.然而,这种背景模型不适用于中等比例尺的航空影像.本文通过对背景模型的改进,把自然地物和人工地物都视为背景,而把阴影视为检测目标,可以很好地实 现建筑物的阴影检测,然后采用阴影补偿法来检测建筑物的变化.试验表明了本方法的有效性.

DOI

[ Ji S ., Yuan X X.A method for shadow detection and change detection of man-made objects[J]. Journal of Remote Sensing, 2007,3(5):323-329. ]

[8]
苏娟,王贵锦,林行刚,等.基于多时相遥感图像的人造目标变化检测算法[J].自动化学报,2008,34(9):1040-1046.<FONT face=Verdana>传统的像素级变化检测方法的检测性能受到以下因素的严重制约: 图像辐射差异、配准误差和差异图像分类门限的选取, 并且难以从检测信息中提取出关键的变化. 本文针对遥感图像中人造目标的变化检测问题, 提出了一种综合特征级和像素级的两步变化检测算法. 首先将大幅多时相遥感图像分成一系列子图像对, 采用有监督子图像对分类方法, 提取人造目标变化的感兴趣区域, 然后采用像素级变化检测算法对感兴趣区域进行变化检测, 得到定量的检测结果. 实验结果表明了该算法的可行性和有效性.</FONT>

DOI

[ Su ., Wang G ., Lin X ., et al.A change detection algorithm for man-made objects based on multi-temporal remote sensing images[J]. Acta Automatica Sinica, 2008,34(9):1040-1046. ]

[9]
Bouziani ., Goita ., He D C.Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010,65(1):143-153.lt;h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">The updating of geodatabases (GDB) in urban environments is a difficult and expensive task. It may be facilitated by an automatic change detection method. Several methods have been developed for medium and low spatial resolution images. This study proposes a new method for change detection of buildings in urban environments from very high spatial resolution images (VHSR) and using existing digital cartographic data. The proposed methodology is composed of several stages. The existing knowledge on the buildings and the other urban objects are first modelled and saved in a knowledge base. Some change detection rules are defined at this stage. Then, the image is segmented. The parameters of segmentation are computed thanks to the integration between the image and the geodatabase. Thereafter, the segmented image is analyzed using the knowledge base to localize the segments where the change of building is likely to occur. The change detection rules are then applied on these segments to identify the segments that represent the changes of buildings. These changes represent the updates of buildings to be added to the geodatabase. The data used in this research concern the city of Sherbrooke (Quebec, Canada) and the city of Rabat (Morocco). For Sherbrooke, we used an Ikonos image acquired in October 2006 and a GDB at the scale of 1:20,000. For Rabat, a QuickBird image acquired in August 2006 has been used with a GDB at the scale of 1:10,000. The rate of good detection is 90%. The proposed method presents some limitations on the detection of the exact contours of the buildings. It could be improved by including a shape post-analysis of detected buildings. The proposed method could be integrated into a cartographic update process or as a method for the quality assessment of a geodatabase. It could be also be used to identify illegal building work or to monitor urban growth.</p>

DOI

[10]
方针,张剑清,张祖勋.基于城区航空影像的变化检测[J].武汉测绘科技大学学报,1997,22(3):240-244.提出了一种城区人工地物变化检测方法。其基本思想是：首先用较简单方法进行变化检测，所检测出的变化都作为待选的变化，然后进行进一步的分析比较，从这些待选的变化中找出我们感兴趣的人工地物的变化。实验结果证明了方法的有效性和可靠性。

DOI

[ Fang ., Zhang J ., Zhang Z X.Change detection based on aerial image of urban area[J]. Journal of Wuhan Technical University of Surveying and Mapping, 1997,22(3):240-244. ]

[11]
耿忠. 面向单波段高分辨率遥感影像的人工目标变化检测技术研究[J].地理信息世界,2007(6):36-41.

[ Geng Z.Research on artificial object changing detection techniques of single-band oriented high resolution remote sensing image[J]. Geomatics World, 2007,6:36-41. ]

[12]
汪闽,张星月.多特征证据融合的遥感图像变化检测[J].遥感学报,2010,14(3):558-570.提出了一种以证据理论综合利用图像多种特征的变化检测方法.方法利用滑动窗口计算两时相图像3种特征的结构相似度,以之构建D-S证据理论的基本概率赋值函数并进行证据合成,通过规则判定得到图像变化区域.通过对不同试验区、不同证据组合方式以及方法间的比较实验表明,相对单一特征检测方法有效地提高了检测的精度.此外,由于采用统计而非原始图像特征度量特征相似性,方法具有对辐射、几何配准精度要求较低等优点.

[ Wang ., Zhang X Y.Change detection using high spatial resolution remotely sensed imagery by combining evidence theory and structural similarity[J]. Journal of Remote Sensing, 2010,14(3):558-570. ]

[13]
Blaschke T.Object based image analysis for remote sensing[J]. Journal of Photogrammetry and Remote Sensing, 2010,65(1):2-16.Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of 鈥榞rey鈥 literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.

DOI

[14]
Chen ., Hay G ., Carvalho L M T, et al. Object-based change detection[J]. International Journal of Remote Sensing, 2012,33(14):4434-57.

[15]
Lu ., Mausel ., Brond Zio ., et al.Change detection techniques[J]. International Journal of Remote Sensing, 2004,25(12):2365-2401.

[16]
劳小敏. 基于对象的高分辨率遥感影像土地利用变化检测技术研究[D].杭州:浙江大学,2013.

[ Lao X M.The research of object-based high resolution remote sensing land use change detection[D]. Hangzhou: Zhejiang University, 2013. ]

[17]
王琰. 基于像斑统计分析的高分辨率遥感影像土地利用/覆盖变化检测方法研究[D].武汉:武汉大学,2012.

[ Wang Y.The research of land use/cover change detection using high resolution remote sensing images based on image segments statistical analysis[D]. Wuhan: Wuhan University, 2012. ]

[18]
Zhang L ., Huang ., Huang ., et al.A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006,40(10):2950-2961.Not Available

DOI

[19]
Pesaresi ., Gerhardinger ., Kayitakire F.A robust built-up area presence index by anisotropic rotation-invariant textural measure[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2008,1(3):180-92.A procedure for the calculation of a texture-derived built-up presence index (PanTex) from textural characteristics of panchromatic satellite data is presented. The index is based on fuzzy rule-based composition of anisotropic textural co-occurrence measures derived from the satellite data by the gray-level co-occurrence matrix (GLCM). Examples are produced how the PanTex index reduces the edge effects of the nonbuilt-up linear features and improves capacity to discriminate between built-up and nonbuilt-up areas. The accuracy and robustness of the PanTex measure against seasonal changes, multisensor, multiscene, and data degradation by wavelet-based compression and histogram stretching is discussed with some examples.

DOI

[20]
Pesaresi ., Gerhardinger A.Improved textural built-up presence index for automatic recognition of human settlements in arid regions with scattered vegetation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011,4(1):16-26.The so-called PANTEX methodology for the automatic recognition of built-up areas is based on analysis of image textural measures extracted using anisotropic rotation-invariant gray-level co-occurrence matrix (GLCM) statistics [2]. These measures may overestimate the built-up areas in case of presence of scattered trees having the same spatial pattern of settlements. This overestimation is especially remarkable in case of bright soil background as in desert areas. In this paper we compare two options able to reduce this problem. One method is based on the subtraction of the vegetated areas from the built-up areas detected using the PANTEX index. The other method is based on the introduction of a morphological filtering step that pre-selects the image information to be ingested by the textural analysis phase. The test presented here uses multispectral Quick Bird satellite data input at the spatial resolution of 2.4 meters. In the selected test area, the application of the standard PANTEX procedure achieves the overall accuracy of 67.92%. The improvement of the procedure using the vegetation index achieves the accuracy of 70.37%, while the improvement based on morphological filtering achieves the accuracy of 88.69%, with an increase respect to the standard procedure of 2.44% and 20.76%, respectively.

DOI

[21]
武斌. 土地利用图斑约束的新增建设遥感提取方法研究[D].北京:中国科学院遥感与数字地球研究所,2014.

[ Wu B.Research of extracting newly increased built-up area based on land-use maps[D]. Beijing: Chinese Academy of Science, the Institute of Remote Sensing and Digital Earth, 2014. ]

Options
Outlines

/

〈 〉