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Journal of Geo-information Science >

2018 , Vol. 20 >Issue 1: 99 - 107

DOI: https://doi.org/10.12082/dqxxkx.2018.170177

Effects of Spatial Resolution and Texture Features on Multi-spectral Remote Sensing Classification

  • YANG Yanjun , 1, 2, 3 ,
  • TIAN Qingjiu , 1, 2, * ,
  • ZHAN Yulin 4 ,
  • TAO Bo 3 ,
  • XU Kaijian 1, 2
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  • 1. International Institute for Earth System Science, Nanjing University, 210023 Nanjing, China
  • 2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 210023 Nanjing, China
  • 3. University of Kentucky, 40546 Lexington, USA
  • 4. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 100101 Beijing, China
*Corresponding author: TIAN Qingjiu, E-mail:

Received date: 2017-05-05

  Request revised date: 2017-09-24

  Online published: 2018-01-20

Supported by

National Natural Science Foundation of China, No.41771370;High-resolution Earth Observation System Project of China, No.30-Y20A29-9003-15/17, 03-Y20A04-9001-15/16, 03-Y20A04-9001-17/18;National Key R&D Program of China, No.2017YFD0600903.

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Abstract

Multi-spectral remote sensing classification is strongly affected by spatial resolution while the classification accuracy is not necessarily improved by the increase of the spatial resolution. There exists an optimal resolution for each geographical entity, corresponding to its intrinsic spatial and spectral characteristics. Despite of many existing efforts, it is still far from clear how spatial resolutions affect classification accuracy. In recent studies, texture feature has been widely used as an effective factor to increase the classification accuracy of multi-spectral remote sensing. As an important characteristic of spatial information, texture feature is closely linked with morphology and distribution of objects. It may greatly increase classification accuracy in some cases that the same object has different spectra or different objects have the same spectrum. However, large uncertainties still exist in the effects of texture feature on classification for various objects at different spatial scales. This paper presents a case study implemented in Jining, Shandong Province to examine the impacts of spatial resolution and texture features on Multi-spectral Remote Sensing Images Classification using the Chinese Gaofen-1 (GF-1) satellite data. The GF-1 satellite was successfully launched on April 26, 2013, which was equipped with two types of sensors. One is the wide field view sensor (WFV sensor); the other is the panchromatic and multispectral sensor (PMS sensor) which can acquire panchromatic images at 2 m spatial resolution and multispectral images at 8 m spatial resolution. First, we carried out radiometric calibration, atmospheric correction and precise geometric rectification for original images. Then, we conducted data fusion based on Gram-Schmidt transformation and performed the expansion of spatial scales for the establishment of the spatial series of the reflectance (2~10 m at an interval of 1 m, 10~90 m at an interval of 10 m). Second, we generated the classification results using three popular approaches, i.e., the Maximum Likelihood Classification (MLC), the Support Vector Machine (SVM) and the Artificial Neural Network (ANN). Third, after the calculation of texture features of 2 m and 8 m for reflectance images, separately, the Principal Components Analysis (PCA) method was used for texture features selection. The data combining key features with corresponding multi-spectral bands were classified based on the ANN approach. Finally, we evaluated the classification accuracies using the confusion matrix. Our final regression analysis suggested an optimal spatial resolution of 5 m for the multi-spectral classification, implying that the optimal selection of the spatial resolution is not affected by the spectral information of multi-spectral remote sensing images. Further analysis of changing trend of accuracies along with spatial resolution showed a sharp decrease when the spatial resolution is coarser than 20-30 m. The results of the impacts of texture feature suggested that, compared with the classification by spectral information, the accuracies of winter wheat, architecture, forest and water were increased by 1.49%, 1.51%, 4.94% and 1.54% at 2 m resolution, and 2.95%, 10.95%, 5.91% and 5.14% at 8 m resolution, respectively, when the texture features were introduced. We concluded that, compared with the classification accuracy of the spectral information, considering texture feature effects may improve the classification accuracies, to varying degrees, at different spatial resolutions, especially when an appropriate resolution was chosen. Our findings are practically helpful for the optimal selection of spatial resolution for multi-spectral remote sensing classification. In the next step, we will further examine the impacts of spatial resolution and texture features at larger scales. In addition, the impacts of texture features at different phenological stages will also be investigated.

Key words： Remote sensing classification; texture features; spatial resolution; reflectance series; spectral information

Cite this article

YANG Yanjun , TIAN Qingjiu , ZHAN Yulin , TAO Bo , XU Kaijian . Effects of Spatial Resolution and Texture Features on Multi-spectral Remote Sensing Classification[J]. Journal of Geo-information Science, 2018 , 20(1) : 99 -107 . DOI: 10.12082/dqxxkx.2018.170177

1 引言

随着现代遥感技术的发展,多空间分辨率和多光谱分辨率的遥感图像得到广泛应用。遥感分类一直是遥感领域的热点问题,但是大量遥感影像的出现造成了分类数据空间分辨率选择困难[1]。遥感在空间理论发展中考虑了尺度与地理实体固有的空间属性,因此,由地学现象的尺度本身出发,选择遥感影像的最佳空间分辨率具有现实意义[2,3]。随着空间分辨率的提高,遥感分类精度在一定程度上有了提高,但高空间分辨率给信息提取也面临着新的挑战,空间分辨率越高并不能保证遥感地物分类的精度越高,关键是选择与地物尺度相适宜的最佳空间分辨率[4]。目前,有关多光谱分类与空间分辨率关系的研究主要集中在高空间分辨率范围[5,6,7,8],而多光谱分类在更详细的空间尺度范围以及中等空间分辨率下的分类精度变化鲜有探究。因此,本文结合在中高分辨率范围的遥感分类精度分析空间分辨率对多光谱遥感分类精度的影响,为多光谱遥感分类数据空间分辨率的选择提供了有效的参考,同时对提高多光谱遥感数据的利用效率具有重要的现实意义。
随着近年高空间分辨率数据的增多,纹理特征作为遥感图像的重要信息和基本特征被广泛关注并应用于遥感分类[9]。通过增加纹理特征波段,增加分类源数据信息,增大不同地类之间的区分度,利用更多的信息可以有效地提高分类精度[10,11]。当目标的光谱特征比较接近时,光谱可分性降低,纹理信息对于区分目标会起到积极的作用,纹理的增加也可以在一定程度上促进“异物同谱、同物异谱”问题的解决[12]。此外,由于地物类型的复杂性,不同地物类型在不同空间尺度上的表现也不同[13],且纹理表达的是影像的空间特征,地物类型的形态分布等与纹理特征息息相关,纹理的加入可能对不同空间分辨率下、不同地物分类精度产生不同影响,引入纹理特征分类之前需要考虑。但是,目前有关纹理特征对不同地物类型分类精度影响的相关研究还不多见,因此本文选用4种典型地类探究其分类精度受纹理特征的影响。
本文在山东省选择试验区,基于高分一号2 m全色和8 m多光谱影像,经过数据融合得到高空间分辨率多光谱数据,并对其进行尺度扩展构建包含光谱信息的反射率空间序列,选择当前较有代表性的3种分类方法最大似然法（Maximum Likelihood Classification,MLC）、支持向量机（Support Vector Machine,SVM）、人工神经网络（Artificial Neural Network,ANN）对序列分类[14]。然后,利用灰度共生矩阵计算2 m融合数据及8 m多光谱数据的纹理特征,利用主成分分析（Principle Component Analysis, PCA）提取关键特征与多光谱波段组合并采用ANN方法对其分类,最后计算混淆矩阵验证结果精度。基于分类结果对以下2方面进行分析：① 多光谱遥感分类最佳空间分辨率以及分类精度随空间分辨率变化的变化趋势;② 在2 m和8 m空间分辨率下,纹理特征对不同地物分类精度的影响。

2 研究区概况与数据源

2.1 研究区概况

研究区位于山东省济宁市（东经115°50′40″~117°03′10″,北纬 34°26′40″~35°35′30″）,地处黄淮海平原与鲁中南山地交接地带（图1）。地貌类型以平原为主,土壤类型丰富。该地区属温带大陆性季风气候,日照充足,四季分明;年平均气温为 13.6 ℃,年平均无霜期为170~220 d。年平均降水量大约为500~800 mm,降水主要集中于夏季,春旱夏涝灾害经常发生。黄淮海平原是中国冬小麦主产区之一,研究区农作物亦以冬小麦为主[15]。
Fig. 1 The map of the study area

图1 研究区概况

2.2 数据源

中国陆地资源卫星高分一号卫星（GF-1）于2013年4月26日在酒泉卫星发射中心成功发射,是中国首颗民用高分辨率卫星。卫星搭载的全色多光谱相机（Panchromatic and Multispectral Sensor, PMS）全色波段可以达到2 m空间分辨率,多光谱波段可以达到8 m空间分辨率,单景幅宽30 km,多光谱谱段共设置4个波段,光谱范围为0.45~0.89 μm （蓝光：0.45~0.52 μm,绿光：0.52~0.59 μm,红光：0.63~0.69 μm,近红外：0.77~2.5 μm） [16]。同Moderate Resolution Imaging Spectroradiometer（MODIS）、 HJ-1、Landsat相比,高分一号卫星系统的发射是对地球表面资源进行遥感解译研究的又一重大举措,其空间分辨率相对以往卫星产品有了很大的提高,为遥感科学研究及其应用提供了质量更高的数据[17]。
研究表明,绿色植物的反射光谱曲线在蓝光波段和红光波段各有一个叶绿素吸收带,在近红外波段则呈现高反射,是区分于非植被的典型特征[18]。本研究结合2016年4月在山东省的实地考察,以及GF-1数据获取情况,根据遥感影像数据的可得性、可操作性、可比性等原则,选取云量小于10%的影像开展工作,采用2016年4月13日的遥感影像,文件名文件名为GF1_PMS1_E116.9_N35.5_20160413_ L1A0001521880。
2016年4月在研究区开展野外试验,采集了大量典型地物类型的样点,获取了研究区4种地物类型分布的样点共2065个（表1）,并利用手持精度1 m的GPS采集用于几何精校正的控制点30个。
Tab. 1 Sample numbers

表1 样本数量

类别 样本
训练样本 验证样本
冬小麦 120 804
有林地 90 413
水体 98 130
人工建筑 80 330
总计 388 1677

3 研究方法

3.1 数据预处理

首先,对多光谱影像辐射定标,将DN值转成辐亮度,然后借助ENVI/IDL的 FLAASH模块进行大气校正得到地表反射率,并选用二次多项式模型,利用地面控制点对高空间分辨率影像进行几何精校正[19],再利用校正好的影像校正多光谱影像,误差控制在小于1个像元。之后,基于Gram-Schmidt对全色和多光谱数据进行影像融合,充分利用全色的高空间分辨率和多光谱的光谱信息,融合后的影像既保持了多光谱影像丰富的光谱信息,同时提高了空间分辨率、增强了清晰的纹理特征[20]。

3.2 反射率空间序列构建

采用同一幅影像进行尺度扩展保证整个空间序列数据源相同,减少序列构建过程中的误差。为了最大程度地保持影像的光谱信息,选用信息丢失最少的最邻近插值法[21]。该方法计算简单,速度快且不会改变原始栅格值,由于对影像信息量的影响较小,尤其适合分类前使用[22]。由融合后的高空间分辨率多光谱影像重采样得到一组反射率空间序列用于开展分类研究（即2、3、4、5、6、7、8、9、10、20、30、40、50、60、70、80和90 m）。

3.3 纹理特征计算

应用灰度共生矩阵（Gray Level Co-occurrence Matrices,GLCM）描述影像灰度值的空间关系和结构特征,该方法能够较好地体现图像灰度统计规律[23]。采用3×3窗口对2 m融合数据及8 m多光谱数据计算纹理特征[24]。由于波段数量太多容易造成数据冗余,不利于分类研究,故采用主成分分析（Principal Component Analysis,PCA）,选取关键特征[25]。结果显示,前3个波段包含了95%的信息量,因此分别选用2 m和8 m分辨率下的前3个主成分与相应反射率波段组合用于探究纹理特征对分类的影响。

3.4 样本选择

根据野外试验,获取研究区4种典型地物的分布样本,包括冬小麦、有林地、水体、人工建筑（表1）。由于分类结果会受训练样本的影响,所以训练样本的选择应该具有代表性。同时为确保样本的纯度应尽量选择地物类型的中心点,尤其对于90 m分辨率的数据。每个类型的训练样本个数至少是本身数据维数的10-30倍才能够包含足够的信息正确描述地物类型的均值和方差[26,27]。为了保证分类结果的可对比性,分类过程需要采用同一组训练样本,且应该选用不同于训练样本的另外一组验证样本对所有分类结果进行精度评价（各地物类型的训练样本与验证样本数量见表1）。
JM 距离是基于条件概率理论的可分性评价指标,适合于表达类别可分性,公式如下[28]：
J M ij = ∫ x p ( X / ω i ) - p ( X / ω j 2 dX 1 2 （1）
式中： p ( X / ω i ) 为条件概率密度,即第i个像元属于第ωi个类别的几率。
计算每2个地类间的JM距离,JMij范围是0-2,代表样本间可分离程度。分离度大于1.90时,说明具有很好的可分离性[29]。如图2所示,整个反射率序列的所有JM值都大于1.90,其中冬小麦与建筑、冬小麦与水体的可分性非常好,JM值达到了2;图3是2 m和8 m分辨率下不同地类之间的分离度,所有JM值都大于1.95。以上说明4种地类的训练样本之间具有很好的分离度,可以用于开展分类研究。
Fig. 2 Jeffreys-Matusita distance of four typial classes at different spatial resolutions

图2 不同空间分辨率下4种地物之间的JM值

Fig. 3 Jeffreys-Matusita distance of four typial classes at 2 m and 8 m spatial resolutions

图3 2 m和8 m分辨率下4种地类之间的JM值
注：冬指冬小麦;林指有林地;建指人工建筑;水指水体

3.5 分类方法

选用最大似然分类（Maximum Likelihood Classification,MLC）、支持向量机分类（Support Vector Machine,SVM）、人工神经网络分类（Artificial Neural Network,ANN）开展反射率空间序列分类。基于参数化密度分布模型的MLC是遥感影像分类最常用手段之一,它具有清晰的参数解释能力、易于与先验知识融合、算法简单、分类精度较高,和计算时间快等优点[30]。SVM是一种不通过概率密度估计直接构造分类面的全新的模式识别方法,通过引进核函数把低维特征空间中的样本数据映射到高维特征空间来实现分类,常被用于分类回归分析,具有突出的分类性能[31]。ANN是一种非参数的分类方法,具有良好的适应能力和复杂的映射能力,以模拟大脑结构和机能为基础,实现非线性的数据模式识别,能够有效地结合影像的光谱和纹理特征提高分类精度[32]。

3.6 精度评价

利用相同的验证样本对分类精度进行精度检验。采用拟合分析方法对反射率空间序列分类的结果进行对比分析,并对2 m和8 m分辨率下纹理特征加入前后的分类结果进行精度评价。选取混淆矩阵的4个指标进行表示,包括Kappa系数（Kappa Coefficient）、总体分类精度（Overall Accuracy, OA）、制图精度（Producer’s Accuracy, PA）和用户精度（User’s Accuracy, UA）[33]。

4 结果与讨论

4.1 空间分辨率对多光谱遥感分类精度的影响

从反射率空间序列分类结果可以看出（图4）,ANN分类精度最高,其次是SVM,MLC的精度最低。利用单景影像的光谱信息分类,ANN方法得到的最高精度达到92.38%,Kappa系数达到0.8861。
Fig.4 Overall accuracy of the classification for three methods at different spatial resolutions

图4 3种分类方法的总体精度曲线及相应趋势线
注：Poly.（MLC） 代表MLC总体分类精度的趋势线;Poly. （SVM） 代表SVM总体分类精度的趋势线;Poly. （ANN） 代表ANN总体分类精度的趋势线

回归拟合可以准确表达2个变量之间的关系,由于受空间数据的限制以及数据重采样等过程造成的误差影响,不能无限地加密空间序列,而采用拟合法可以定量分析多光谱遥感分类的最佳空间分辨率。通过添加趋势线分析整个反射率空间序列的总体精度变化趋势,选用拟合程度最高的二次多项式方程求得趋势线的最大值作为最佳空间分辨率,如式2所示。
y MLC = - 0.0931 x 2 + 0.8757 x + 89.094 R 2 = 0.94 y SVM = - 0.0900 x 2 + 0.8841 x + 90.242 R² = 0.97 y ANN = - 0.1021 x 2 + 1.0979 x + 89.684 R² = 0.90 （2）
x MLC = 4.7 x SVM = 4.9 x ANN = 5.3
通过对总体精度的拟合曲线分析,得到利用3种分类方法对反射率分类的最佳空间分辨率在5 m,有学者利用IKONOS影像得到不同地物类型的最佳空间分辨率范围在3~7 m,而本文通过对更广的空间尺度遥感影像分类分析,进一步明确了地物遥感分类的最佳空间分辨率,本文的研究结果比先前学者得到的结论更加精确[6,34],这说明遥感分类的最佳空间分辨率不会受到光谱信息的影响,同时也证明了回归分析可以用于对遥感空间序列分类精度的拟合分析。
从图4可看出,随着空间分辨率的变化3种分类方法的总体精度变化趋势大致相同,但在不同的空间分辨率范围内,分类精度变化的速度不同。空间分辨率在2~20 m时,随着空间分辨率的降低3种分类法的精度变化较为稳定,降低趋势缓和,且总体分类精度一致保持在90%以上。但是当空间分辨率低于20 m后,MLC和SVM分类精度快速降低到90%以下,当空间分辨率低于30 m时,ANN的分类精度也随之降低到90%以下,在整个空间序列内分辨率在20~30 m区间分类精度变化最为显著,因此,20~30 m空间分辨率范围是多光谱遥感分类获得较高精度的一个重要临界位置,由此可以为多光谱遥感分类数据空间分辨率的选择提供有效参考。

4.2 纹理特征对不同地物分类的影响

利用ANN分类方法对2 m和8 m空间分辨率的反射率数据及纹理与反射率结合数据分别进行分类,分类结果如图5所示,精度如表2所示。其中,PA指制图精度,UA指用户精度,OA指总体分类精度。
Fig.5 Classification results before and after introducing texture features at 2 m and 8 m spatial resolution

图5 2 m和8 m分辨率下纹理特征加入前后的分类结果图

Tab. 2 Classification accuracy of four classes

表2 4种典型地类的分类精度

数据类别 OA/% Kappa系数 冬小麦/% 有林地/% 水体/% 人工建筑/%
PA UA PA UA PA UA PA UA
2 m反射率 91.95 0.8789 93.91 94.49 89.10 82.33 96.92 94.03 88.79 98.65
2 m反射率_纹理 93.62 0.9000 95.40 95.76 91.28 87.27 98.46 89.51 90.30 99.00
8 m反射率 90.59 0.8585 93.03 94.44 88.94 78.19 96.92 94.03 84.19 98.58
8 m反射率_纹理 93.64 0.9100 92.78 97.39 94.84 84.10 91.54 99.17 95.14 96.31
从分类结果精度可看出,利用光谱与纹理特征结合分类精度高于仅利用光谱特征分类的精度,说明纹理特征与光谱信息结合可以提高遥感分类精度,但不同分辨率下精度变化不同,引入纹理特征后,在8 m分辨率下的总体分类精度高于2 m分辨率的精度,同时我们发现,纹理特征的加入对4种地类的影响程度各不相同。利用公式计算引入纹理特征前后的精度变化,如图6所示。
C A i = A C tei - A C rei （3）
式中：CAi指空间分辨率为i时的精度变化;ACrei和ACtei分别指空间分辨率为i时加入纹理前后的分类精度。
Fig. 6 Accuracy changes of four classes before and after adding texture features

图6 添加纹理特征前后各地物的精度变化
注：PA 代表制图精度,UA代表用户精度

对照引入纹理特征前后的分类精度,分析在不同空间分辨率下纹理特征的加入对4种地类分类精度的影响。① 在2种空间分辨率下,纹理特征加入前后冬小麦分类精度都在90%以上,可能与本文的数据时间选择有一定关系,春季是冬小麦识别的最佳时期,该时期冬小麦与其他地类的光谱信息差异最大,区分明显[35]。纹理特征加入后,冬小麦分类精度在2 m分辨率下提高了1.47%,在8 m分辨率下提高2.95%,纹理特征对冬小麦分类精度作用不明显,尤其在空间分辨率为2 m时,破碎度较高的小地块易形成混合像元,导致纹理特征加入后其精度变化不大。② 水体在不同分辨率下的分类精度都较高,尤其是引入纹理特征前水体分类就达到了较高的精度,这与水体的光谱特征密切相关,由于水体的光谱反射率较低,与其他地类差异最为明显,仅利用光谱信息就可以达到较为理想的分类精度。如图6,加入纹理特征后,2种空间分辨率下的水体的分类精度都没有较大的改善,在2 m分辨率下用户精度降低了4.52%,制图精度提高了1.54%,在8 m分辨率下用户精度提高了5.14%,制图精度降低了5.38%,与其他地类相比,纹理特征的引入对水体分类精度没有明显的改善。在图5中（a）与（b）、（c）与（d）相比,在图5（b）和图5（d）中分类结果水体区域增加,但与实际情况对比,增加的部分并非水体,由此可见纹理特征的加入对水体而言成为了冗余信息,使其与其他地物的差异程度降低,因此水体的分类精度没有得到明显改善,冗余信息的加入甚至加重了水体的错分现象。③ 纹理特征对人工建筑分类的影响作用最显著,在8 m空间分辨率下人工建筑分类精度提高了10.95%,可能在该空间分辨率下人工建筑的形态较为均一,纹理特征明显,但是纹理的加入对2 m分辨率的人工建筑分类精度仅提高了1.51%,说明纹理特征对分类精度的影响与空间分辨率有着密切的关系。④ 仅利用光谱信息分类的有林地精度最低,虽然利用反射率对有林地的分类效果不理想,但是添加纹理特征后精度有了较大程度的改善,在2 m分辨率下,UA和PA分别提高了2.18%和4.94%,在8 m分辨率下UA和PA分别提高了5.90%和5.91%,2种评价精度同时得到提高说明纹理特征有助于提高有林地的分类效果。虽然加入纹理后有林地分类精度提高幅度较大,但是用户精度仍然低于85%,所以有林地分类精度的提高需要进一步探究。综上,不同地物类型受纹理特征的影响程度不同,并与空间分辨率有着密切关系,因此纹理特征对多光谱遥感分类的影响并不是分辨率越高分类精度越高,在适宜的分辨率下进行分类识别才能达到更好的效果。

5 结论

本研究通过分析2~90 m反射率空间序列的 分类精度,采用回归拟合得到多光谱遥感分类的最佳空间分辨率在5 m,并分析了分类精度随空间分辨率变化的变化趋势,研究结果发现在该序列内20~30 m范围是分类精度变化最显著的区域,这对多光谱遥感分类空间分辨率的选择提供了有力参考。同时,分析了2 m和8 m空间分辨率下纹理特征的加入对不同地类分类精度的影响。通过对比 4种典型地类分类精度发现,纹理特征的加入对不同空间分辨率、不同地类分类精度的影响不同,且在8 m空间分辨率下的各地类的分类精度提高程度大于2 m分辨率的。结果表明,可以结合光谱信息及纹理特征,针对不同地物类型通过对空间分辨率的优选提高分类精度。
今后将尺度序列的范围及研究区范围进一步扩大,探究粗空间分辨率遥感分类的规律。同时,纹理特征对不同分辨率下的分类精度影响不同,究竟分辨率为多少时最佳还需要进一步研究。另外,针对植被类型,不同生长期的数据对其有一定程度的影响,不同物候期,植被纹理特征不同,与其他地类的区分度也不同,纹理特征在不同物候期对植被分类的影响程度也需要进一步研究。

The authors have declared that no competing interests exist.

References

Publishing order | Descend order by publishing year | Descend order by cited within
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[16]
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[17]
Li X, Ling F, Du Y, et al.A spatial-temporal hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images[J]. ISPRS Journal of Photogrammetry and remote Sensing, 2014,93:76-87.The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial emporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.

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[18]
Dash J, Jeganathan C, Atkinson PM.The use of MERIS terrestrial chlorophyll index to study spatio-temporal variation in vegetation phenology over India[J]. Remote Sensing of Environment, 2010,114(7):1388-402.India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. The phenology of these natural vegetation types is often controlled by climatic condition. Estimating phenological variables will help in understanding the response of tropical and subtropical vegetation to climate change. The study investigated the annual and inter-annual variation in vegetation phenology in India using satellite remote sensing. The study used time-series data of the only available satellite measured index of terrestrial chlorophyll content (MERIS Terrestrial Chlorophyll Index) from 2003 to 2007 at 4.6 km spatial resolution. A strong coincidence was observed with expected phenological pattern, in particular, in inter-annual and latitudinal variability of key phenological variables. For major natural vegetation type the onset of greenness had greater latitudinal variation compared to the end of senescence and there was a small or no leafless period. In the 2003 04 growing season a late start for the onset of greenness was detected at low-to-mid latitudes and it was attributed to the extreme cold weather during the early part of 2003. The length of growing season varied from east to west for the major cropping areas in the Indo-Gangetic plain, for both the first and second crops. For the first time, this study attempted to establish a broad regional phenological pattern for India using remotely sensed estimation of canopy chlorophyll content using five years of data. The overall patterns of phenological variables detected from this study broadly coincide with the pattern of natural vegetation phenology revealed in earlier community level studies. The results of this study suggest the need for an organised network combining ground and space observation which is at presently missing in India.

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[19]
Yang Y J, Huang Y, Tian Q J, et al.The extraction model of paddy rice information based on GF-1 satellite WFV images[J]. Spectroscopy and Spectral Analysis, 2015,35(11):3255-3261.In the present,using the characteristics of paddy rice at different phenophase to identify it by remote sensing images is an efficient way in the information extraction.According to the remarkably properties of paddy rice different from other vegetation,which the surface of paddy fields is with a large number of water in the early stage,NDWI(normalized difference water index)which is used to extract water information can reasonably be applied in the extraction of paddy rice at the early stage of the growth.And using NDWI ratio of two phenophase can expand the difference between paddy rice and other surface features,which is an important part for the extraction of paddy rice with high accuracy.Then using the variation of NDVI(normalized differential vegetation index)in different phenophase can further enhance accuracy of paddy rice information extraction.This study finds that making full advantage of the particularity of paddy rice in different phenophase and combining two indices(NDWI and NDVI)associated with paddy rice can establish a reasonable,accurate and effective extraction model of paddy rice.This is also the main way to improve the accuracy of paddy rice extraction.The present paper takes Lai'an in Anhui Province as the research area,and rice as the research object.It constructs the extraction model of paddy rice information using NDVI and NDWI between tillering stage and heading stage.Then the model was applied to GF1-WFV remote sensing image on July 12,2013 and August 30,2013.And it effectively extracted out of paddy rice distribution in Lai'an and carried on the mapping.At last,the result of extraction was verified and evaluated combined with field investigation data in the study area.The result shows that using the extraction model can quickly and accurately obtain the distribution of rice information,and it has the very good universality.

DOI PMID

[20]
Vivone G, Alparone L, Chanussot J, et al.A critical comparison among pan sharpening algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,53(5):2565-2586In this paper state-of-the-art and advanced methods for multispectral pansharpening are reviewed and evaluated on two very high resolution datasets acquired by IKONOS-2 (four bands) and WorldView-2 (eight bands). The experimental analysis allows us to highlight the performances of the two main pansharpening approaches (i.e. component substitution and multiresolution analysis).

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[21]
Dalponte M, Bruzzone L, Gianelle D.Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/ hyperspectral images and LiDAR data[J]. Remote Sensing of Environment, 2012,123:258-270.78 Hyperspectral data allow one to distinguish similar species. 78 Downscaling the spectral resolution reduces the discrimination ability of the data. 78 Multispectral data are effective for macro-classes discrimination. 78 The addition of LiDAR data increase the classification accuracy. 78 High point density LiDAR data allow higher class accuracy than low point density data.

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[22]
张周威,余涛,孟庆岩,等.空间重采样方法对遥感影像信息影响研究[J].华中师范大学学报(自科版),2013,47(3):426-430.在对原始影像进行几何校正时需要对影像进行重采样,必然会导致光谱信息的失真,如何保持原始影像的光谱信息一直是研究的热点问题.从影像应用质量评价的角度,对3种不同类型的遥感影像质量进行了评价与分析,通过对多幅不同空间分辨率的遥感影像重采样后进行比较,评价了重采样后遥感影像的质量.研究表明,影像重采样后的信息量随着空间分辨率的提高基本保持不变,随着空间分辨率的降低,信息量也逐步减少的结论,为评价遥感影像重采样后影像信息量损失情况的多尺度效应提供参考.

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[ Zhang Z W, Yu T, Meng Q Y, et al.Research on the effects of spatial resampling method on the information of remote sensing images[J]. Journal of Huazhong Normal University (Nat. Sci.), 2013,47(3):426-430. ]

[23]
Fardin M, Hassan G.Improving hyperspectral image classification by combining spectral, texture, and shape features[J]. International Journal of Remote Sensing, 2015,36(4):1070-1096.Several studies have already demonstrated the efficiency of utilizing spatial information in representation and interpretation of hyperspectral (HS) images. Texture and shape features are known as two important categories of spatial information in various applications of image processing. This study tries to utilize texture and shape features extracted from HS images, as well as spectral information, in order to reduce overall classification errors. These features include morphological profiles (MPs), global Gabor features, and features extracted from conventional and segmentation-based grey-level co-occurrence matrices (GLCMs). Various combinations of these spatial features along with spectral information are fed into a support vector machine (SVM) classifier, and the best combinations for different situations are determined. Experiments on the widely used Indian Pines, Pavia University, and Salinas HS data sets demonstrate the efficiency of the proposed framework in comparison with some recent spectral鈥搒patial classification methods.

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[24]
Kiema J B K. Texture analysis and data fusion in the extraction of topographic objects from satellite imagery[J]. International Journal of Remote Sensing, 2002,23(4):767-776.This paper examines the influence of multisensor data fusion on the automatic extraction of topographic objects from SPOT panchromatic imagery. The suitability of various grey level co-occurrence based texture measures, as well as different pixel windows is also investigated. It is observed that best results are obtained with a 3 2 3 pixel window and the texture measure homogeneity. The synthetic texture image derived together with a Landsat Thematic Mapper (TM) imagery are then fused to the SPOT data using the additional channel concept. The object feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied. It is demonstrated that the segmentation of topographic objects is significantly improved by fusing the multispectral and texture information.

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[25]
Dash M, Liu H.Feature selection for classification[J]. Intelligent Data Analysis: An International Journal, 1997,1(1-4):131-156.

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[26]
Vanniel T G, Mcvicar T R, Datt B.On the relationship between training sample size and data dimensionality of broadband multi-temporal classification[J]. Remote Sensing of Environment, 2005,98(4):468-480.The number of training samples per class ( n ) required for accurate Maximum Likelihood (ML) classification is known to be affected by the number of bands ( p ) in the input image. However, the general rule which defines that n should be 10 p to 30 p is often enforced universally in remote sensing without questioning its relevance to the complexity of the specific discrimination problem. Furthermore, identifying this many training samples is often problematic when many classes and/or many bands are used. It is important, then, to test how this generally accepted rule matches common remote sensing discrimination problems because it could be unnecessarily restrictive for many applications. This study was primarily conducted in order to test whether the general rule defining the relationship between n and p was well-suited for ML classification of a relatively simple remote sensing-based discrimination problem. To summarise the mean response of n -to- p for our study site, a Monte Carlo procedure was used to randomly stack various numbers of bands into thousands of separate image combinations that were then classified using an ML algorithm. The bands were randomly selected from a 119-band Enhanced Thematic Mapper-plus (ETM+) dataset comprised of 17 images acquired during the 2001–2002 southern hemisphere summer agricultural growing season over an irrigation area in south-eastern Australia. Results showed that the number of training samples needed for accurate ML classification was much lower than the current widely accepted rule. Due to the asymptotic nature of the relationship, we found that 95% of the accuracy attained using n02= 0230 p samples could be achieved by using approximately 2 p to 4 p samples, or ≤02102/027th the currently recommended value of n . Our findings show that the number of training samples needed for a simple discrimination problem is much less than that defined by the general rule and therefore the rule should not be universally enforced; the number of training samples needed should also be determined by considering the complexity of the discrimination problem.

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[27]
Foody G M, Mathur A, Sanchez-Hernandez C, et al.Training set size requirements for the classification of a specific class[J]. Remote Sensing of Environment, 2006,104(1):1-14.The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of 65 90% from that suggested by a conventional heuristic are reported with the accuracy of cotton classification remaining nearly constant at 65 95% and 65 97% from the user's and producer's perspectives respectively.

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[28]
Wang C Y, Liu Z J, Yan C Y.An experimental study on imaging spectrometer data feature selection and wheat type identification[J]. Journal of Remote Sensing, 2006,10(2):249-254.With the development of imaging spectrometer technology,the ground objects' consecutive information from it makes it possible to identify different vegetation types,though some relevant research was carried out in the past few years,most are about forestry,yet few about crops.Further,there exist strong correlation between bands of imaging spectrometer,so how to reduce as much as possible the redundant information and reserve useful information appear much more important.This paper first did feature selection based on genetic algorithm(GA) and wheat biophysical characteristics.In feature selection using GA,for the training samples,when combined bands reach 4,the JM distance of optimal combination reach much high level,when bands go on increasing,the average JM distance increases slowly until when bands reach 8,the distance does not increase further,so the optimal bands combination can be obtained.In feature selection using wheat biophysical characteristics,we found that there appear strong correlative bands for wheat protein and dry gluten with spectra,so the sensitive bands can be obtained.Combining these two feature selection steps,the ultimate optimal bands combination was given.After feature selection,we use the selected bands and classifier Fuzzy-Artmap to classify the imaging spectrometer data.It showed that for 4 wheat types,they can be identified clearly,the average classification accuracy is above 90%.

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[29]
齐腊,刘良云,赵春江,等.基于遥感影像时间序列的冬小麦种植监测最佳时相选择研究[J].遥感技术与应用,2008,23(2):154-160.lt;p>遥感影像植被分类的最佳时相对作物种植面积遥感监测非常重要。根据2005~2006年北京冬小麦不同物候期的Landsat TM影像和2006年Spot\|2影像，计算了各时期影像中主要植被类型的光谱可分性距离，分析了北京郊区主要植被物候差异和光谱可分性；对各生育期的遥感影像及其主要组合进行了监督分类，采用总体精度和分类效率指标两个参数，结合地面GPS调查数据，对分类结果进行了精度评价。结果表明：北京地区小麦监测最佳时相是4月上旬，影像分类的总体精度为92.9％，明显优于其它单时相影像的分类结果；发现北京郊区冬小麦光谱分类的最佳时相组合为4月上旬(起身期)和5月下旬(灌浆期)，分类总体精度为94％。</p>

DOI

[ Qi L, Liu L Y, Zhao C J.Study on optimum time selection of the monitoring of winter wheat planting based on the time series of remote sensing images[J]. Remote Sensing Technology Application, 2008,23(2):154-160. ]

[30]
Islam A E, Tanton T W.Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification[J]. International Journal of Remote Sensing, 2003,24(21):4197-4206.

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[31]
Hao P, Zhan Y L, Wang L, et al.Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA[J]. Remote Sensing, 2015,7(5):5347.Currently, accurate information on crop area coverage is vital for food security and industry, and there is strong demand for timely crop mapping. In this study, we used MODIS time series data to investigate the effect of the time series length on crop mapping. Eight time series with different lengths (ranging from one month to eight months) were tested. For each time series, we first used the Random Forest (RF) algorithm to calculate the importance score for all features (including multi-spectral data, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and phenological metrics). Subsequently, an extension of the Jeffries atusita (JM) distance was used to measure class separability for each time series. Finally, the RF algorithm was used to classify crop types, and the classification accuracy and certainty were used to analyze the influence of the time series length and the number of features on classification performance; the features were added one by one based on their importance scores. Results indicated that when the time series was longer than five months, the top ten features remained stable. These features were mainly in July and August. In addition, the NDVI features contributed the majority of the most significant features for crop mapping. The NDWI and data from multi-spectral bands also contributed to improving crop mapping. On the other hand, separability, classification accuracy, and certainty increased with the number of features used and the time series length, although these values quickly reached saturation. Five months was the optimal time series length, as longer time series provided no further improvement in the classification performance. This result shows that relatively short time series have the potential to identify crops accurately, which allows for early crop mapping over large areas.

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[32]
Kumar P, Gupta D K, Mishra V N, et al.Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data[J]. International Journal of Remote Sensing, 2015,36(6):1604-1617.The Resourcesat-2 is a highly suitable satellite for crop classification studies with its improved features and capabilities. Data from one of its sensors, the linear imaging and self-scanning (LISS IV), which has a spatial resolution of 5.8 m, was used to compare the relative accuracies achieved by support vector machine (SVM), artificial neural network (ANN), and spectral angle mapper (SAM) algorithms for the classification of various crops and non-crop covering a part of Varanasi district, Uttar Pradesh, India. The separability analysis was performed using a transformed divergence (TD) method between categories to assess the quality of training samples. The outcome of the present study indicates better performance of SVM and ANN algorithms in comparison to SAM for the classification using LISS IV sensor data. The overall accuracies obtained by SVM and ANN were 93.45% and 92.32%, respectively, whereas the lower accuracy of 74.99% was achieved using the SAM algorithm through error matrix analysis. Results derived from SVM, ANN, and SAM classification algorithms were validated with the ground truth information acquired by the field visit on the same day of satellite data acquisition.

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[33]
Verma N K, Lamb D W, Reid N, et al.A comparative study of land cover classification techniques for “farms capes” using very high resolution remotely sensed data[J]. Photogrammetry Engineering and Remote Sensing, 2014,80(5):461-470.ABSTRACT High spatial resolution images (~10 cm) are routinely available from airborne platforms. Few studies have examined the applicability of using such data to characterize land cover in “farmscapes” comprising open pasture and remnant vegetation communities of varying density. Very high spatial resolution remotely sensed imagery has been used to classify land cover classes on a ~5000 ha extensive grazing farm in Australia. This “farmscape” consisted of open pasture fields, scattered trees, and remnant vegetation (woodlands). The relative performances of object-based and pixel-based approaches to classification were tested for accuracy and applicability. Maximum likelihood classification (MLC) was used for pixel-based classification while the k-nearest neighbor (k-NN) technique was used for object-based classification. A range of image sampling scales was tested for image segmentation. At an optimal sampling scale, the pixel-based classification resulted in an overall accuracy of 77 percent, while the object-based classification achieved an overall accuracy of 86 percent. While both the object- and pixel-based classification techniques yielded higher quantitative accuracies, a “more realistic” land cover classification, with few errors due to intermixing of similar classes, was achieved using the object-based method.

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[34]
Chen J, Deng M, Xiao P F, et al.Optimal spatial scale choosing for high resolution imagery based on texture features frequency analysis[J]. Journal of Remote Sensing, 2011,15(3):492-511.

[35]
Qiu B, Luo Y, Tang Z, et al.Winter wheat mapping combining variations before and after estimated heading dates[J]. ISPRS Journal of Photogrammetry and remote sensing, 2017,123:35-46.Accurate and updated information on winter wheat distribution is vital for food security. The intra-class variability of the temporal profiles of vegetation indices presents substantial challenges to current time series-based approaches. This study developed a new method to identify winter wheat over large regions through a transformation and metric-based approach. First, the trend surfaces were established to identify key phenological parameters of winter wheat based on altitude and latitude with references to crop calendar data from the agro-meteorological stations. Second, two phenology-based indicators were developed based on the EVI2 differences between estimated heading and seedling/harvesting dates and the change amplitudes. These two phenology-based indicators revealed variations during the estimated early and late growth stages. Finally, winter wheat data were extracted based on these two metrics. The winter wheat mapping method was applied to China based on the 250 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) 2-band Enhanced Vegetation Index (EVI2) time series datasets. Accuracy was validated with field survey data, agricultural census data, and Landsat-interpreted results in test regions. When evaluated with 653 field survey sites and Landsat image interpreted data, the overall accuracy of MODIS-derived images in 2012 2013 was 92.19% and 88.86%, respectively. The MODIS-derived winter wheat areas accounted for over 82% of the variability at the municipal level when compared with agricultural census data. The winter wheat mapping method developed in this study demonstrates great adaptability to intra-class variability of the vegetation temporal profiles and has great potential for further applications to broader regions and other types of agricultural crop mapping.

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