[IEEE 2014 Third International Conference on Agro-Geoinformatics - Beijing, China (2014.8.11-2014.8.14)] 2014 The Third International Conference on Agro-Geoinformatics - Single late rice information extraction based on change detection method using neighborhood correlation images

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<ul><li><p>Single Late Rice Information Extraction based on Change Detection method using </p><p>Neighborhood Correlation Images Muyi Li, Xiufang Zhu*, Anzhou Zhao, Xianfeng Liu, Shuchen Chen, Tong Zhou, Yaozhong Pan </p><p>State Key Laboratory of Earth Surface Processes and Resource Ecology; College of Resources Science and Technology, Beijing Normal University Beijing China AbstractOver the past several decades, moderate-resolution </p><p>remote sensing data have been used to obtain regional-scale land-cover information. Nowadays Change detection has been widely used in many applications. Change detection techniques may capture the changes of phenology information which are quite useful to distinguish a certain kind of crop from other land-cover types. In order to extract single late rice through phenology characteristic, a change detection model based on neighborhood correlation images (NCIs) and supervised classification was proposed. In this study, Landsat 8 OLI digital images acquired on July 19, 2013 and November 8, 2013 respectively were used to extract single late rice. Single late rice was chosen as the study object because of its unique phenology information changes, from steeping field to vegetation. The method proposed includes 5 parts: 1) Preprocessing data, 2) Creating NCI images, 3) Collecting training/test samples, 4) Extracting single late rice and 5) Classifying images and assessing accuracy. The overall accuracy of single late rice was 90.3%, indicating that the change detection method using NCIs is an effective way to extract crops with useful phenology information. </p><p>KeywordsNeighborhood Correlation Images; Change Detection; Single late rice; Extraction </p><p>I. INTRODUCTION The change detection technology occurred not long ago in </p><p>remote sensing image processing technology, it is used to recognize the change of the object on land with time pass(Lu, Mausel et al. 2004). And specifically it is the process of identifying differences in the state of an object or phenomenon by observing it at different times(Huang, Zuo et al. 2013), which involves the application of multi-temporal datasets to quantitatively describe the temporal effects of an object. Sometimes, this kind of temporal effect on an object is quite useful to distinguish a certain kind of land-cover type from others(Reed, Brown et al. 1994), especially for crops with unique phenology information, such as rice and wheat. Recently, numerous remote sensing change detection algorithms have been developed(Yin, Wu et al. 2013). However, past studies have focused on change detection of long time serial image data sets, usually with a long time gap(Feng, Xiao et al. 2014). The time gap of two bi-temporal remote sensing images is usually longer than years or even decades. This study brought an entirely new vision to the change detection problem. Crop phenology information was </p><p>taken into consideration in order to extract single late rice. This study has probed a new method to extract crop information from remote sensing data, making full use of phenology information change at two close times. </p><p>II. STUDY SITE AND DATA </p><p>A. Study site Tongxiang city, Zhejiang province was chosen as the study </p><p>site. Tongxiang locates within 12017 to 12039 E longitude and 3028 to 3047 N latitude. The climate there is cold and rainless in winter, hot and rainy in summer. The annual average precipitation ranges from 980 to 1600 mm and annual mean temperature range 15 C to 17 C. Two Landsat8 OLI multispectral images acquired on July 19, 2013 and November 8, 2013 respectively were used in this study. Visual interpretation shows that the dominant land-cover types are crops, pools, intersected with trees, roads, villages and rivers. According to the statistical year book, the main crop type is Single late rice. </p><p>B. Data Landsat 8, collaboration between NASA and the United </p><p>States Geological Survey (USGS) is an American Earth observation satellite launched on February 11, 2013. It is the eighth satellite in the Landsat program. First images from the spacecraft were collected on March 18, 2013. </p><p>Landsat 8's Operational Land Imager (OLI) collects data from nine spectral bands. Seven of the nine bands are consistent with the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors found on earlier Landsat satellites, providing for compatibility with the historical Landsat data, while also improving measurement capabilities. Two new spectral bands, a deep blue coastal / aerosol band and a shortwave-infrared cirrus band are collected in this new sensor. </p><p>In this study Landsat 8's Operational Land Imager (OLI) was selected as the remote sensing data not only because of its high quality, but also because of its wider panchromatic band, which is quite useful to extract vegetation information. </p><p>Corresponding author: zhuxiufang@bnu.edu.cn </p></li><li><p>III. METHODS </p><p>A. The principle of NCI analysis The Single late rice extraction model proposed in this study </p><p>focuses on the phenology information of Single late rice on multi-temporal remote sensing imageries (i.e., the change of spectral characteristic from water to vegetation). The contextual information (i.e., correlation, slope, and intercept in a specified neighborhood) abstained from NCI analysis contributes to the Single late rice extraction model by providing unique change information, among which the change from water to vegetation is of great interest. NCI analysis is based on the change magnitude and direction of brightness values by band in a specific neighborhood between two multispectral remote sensing datasets(Im, Jensen et al. 2005). If the spectral changes of the pixels within a specified neighborhood between two image dates are significant, the correlation coefficient between the two sets of brightness values in the neighborhood will fall off to a lower value. Depending on the magnitude and direction of the spectral changes, slope and intercept values may increase/decrease(Im, Jensen et al. 2005). Equations of correlation, slope, and intercept will be disscussed in the folowing part. </p><p>B. Single late rice extraction based on change detection Fig. 1 summarizes the steps required to implement the extraction of single late rice using change detection method based on neighborhood correlation image analysis and supervised classification. The Single late rice extraction model consists of five analytical parts. </p><p> aIn the first step, multi-temporal remote sensing data should be geometrically and radio metrically preprocessed. After preprocessing the RMSE should be less than 0.5 pixels. </p><p>b. In the second step, Neighborhood Correlation Images (NCIs), i.e., correlation, slope, and intercept images (bands) will be created. </p><p>c. In the third step, training/test samples are collected from the multi-date, multi- channel data set. In this study the ground truth data was obtained by visual interpretation on the Landsat 8 OLI multispectral image with field work. </p><p>d. In the fourth step, single late rice was extracted using the training/test data through maximum likelihood classifier. </p><p>e. In the last step, an accuracy assessment was performed in order to examine the effectiveness of the single late rice extraction model. </p><p>C. Creation of neighborhood correlation images Because the spatial resolution of the remote sensing data used in this study is 15 meters (after fusion), rectangle neighborhood with the size of 3*3 was selected in this study. Neighborhood Correlation Images (i.e., correlation, slope, and intercept images) were created through IDL program. </p><p>The correlation, slope, and intercept between digital number values of two dates of imagery in the local neighborhoods are calculated using the following equations(Im, Jensen et al. 2005): r 1 2 a 3 b 4 </p><p>where r is Pearsons product-moment correlation coefficient, cov12 is the covariance between digital number values found in all bands of the two date datasets in the neighborhood, and s1 and s2 are the standard deviations of the digital number values found in all bands of each data set in the neighborhood, respectively. DNi1 and DNi2 are the ith digital number value of the pixels found in all bands of image 1 and image 2 respectively, n is the total number of the pixels found in all bands of each data set in the neighborhood, and 1 and 2 are the means of digital number values found in all bands of the two date (T1 and T2) images in the neighborhood, respectively. And a, b are slope and intercept calculated by least square method. </p><p>Correlation, slope, and intercept images derived from the multi-temporal images with a rectangle neighborhood of 3-pixel radius are displayed in Fig. 2. </p><p>D. Selection of training/testing samples: The Ground true data for this study was selected using the </p><p>ENVI software. A total of 200 point samples were randomly created with in the study area. The land cover class of each sample location was identified based on the visual interpretation of the multi-date images. Each reference point was assigned to one of the following nine classes: </p></li><li><p> 4 changed classes (FROM-TO CLASS): Steeping field (share the spectral characteristic of water) to Single late rice (share the spectral characteristic of vegetation) (S&gt;V), Barren to Vegetation (B&gt;V), Vegetation to Barren (V&gt;B), and Pool/River to Vegetation (P/R&gt;V) </p><p> 4 unchanged classes (NO CHANGE CLASS): Developed (D), Barren (B), Pool or River (P/R), and Vegetation (V), </p><p>E. Classification and accuracy assessment Maximum likelihood classification method was chosen as </p><p>the supervised classification method in this study. Then the confusion matrix was conducted in order to evaluate the effect caused by the addition of Neighborhood Correlation Images. </p><p>The ground truth data was obtained by visual interpretation on the Landsat8 OLI multispectral image and field work to evaluate the classification result. </p><p>IV. RESULT NCIs created using rectangle neighborhood of 3-pixle was </p><p>used in this study. Maximum like hood classification, one of the traditional supervised classifications was then carried out in order to extract the single late rice information. As mentioned earlier, the purpose was to examine the effectiveness of NCI images. 200 test samples selected by visual interpretation were used to calculate the confusion matrix. </p><p>The classification result was showed in fig.3. The total accuracy of single late rice was 90.3%. </p><p>V. DISCUSSION Generally Nowadays Change detection has been widely </p><p>used in many applications such as urban and regional planning, </p><p>environmental management and forestry. Change detection can also provide information on phenological information of primary crop which is quite useful to distinguish them from other land type. </p><p>This study investigated how NCIs can be utilized in change detection based single late rice extraction model. This study </p><p>(a) Classification result Fig. 3. (a) Classification result based on maximum like hood classification, (b) change detection matrix between two dates used to specify from -to classes </p><p>a. Correlation image b. Slope image c. Intercept image Fig. 2. NCIs created using a rectangle neighborhood with a 3-pixel radius. </p></li><li><p>also demonstrated that NCIs contain change information of crop phenology and that NCIs may be powerful tools for extracting a certain kind of crop with unique phenology characteristic. </p><p>However this model still has a disadvantage. When selecting training samples, all of the FROM-TO / NO CHANGE classes that occur in the study area have to be included in an image classification to ensure the assumption of an exhaustively defined set is satisfied, which is a tough work. Therefor future crop extraction models based on change detection method using NCIs will focus on (a) how to simplify the training samples selecting process(Foody, Mathur et al. 2006), (b) how to make full use of phenology information. </p><p>REFERENCES [1] D. Lu, P. Mausel, E. Brondizio, and E. Moran, "Change detection </p><p>techniques," International Journal of Remote Sensing, vol. 25, pp. 2365-2407, Jun 2004. </p><p>[2] L. Huang, X. Zuo, and X. Yu, "Review on change detection methods of remote sensing images," Science of Surveying and Mapping, vol. 38, pp. 203-206, 2013 2013. </p><p>[3] B. C. Reed, J. F. Brown, D. Vanderzee, T. R. Loveland, J. W. Merchant, and D. O. Ohlen, "MEASURING PHENOLOGICAL VARIABILITY FROM SATELLITE IMAGERY," Journal of Vegetation Science, vol. 5, pp. 703-714, Nov 1994. </p><p>[4] S.-J. Yin, C.-Q. Wu, Q. Wang, W.-D. Ma, L. Zhu, Y.-J. Yao, X.-L. Wang, and D. Wu, "Review of change detection methods using multi-temporal remotely sensed images," Guang pu xue yu guang pu fen xi = Guang pu, vol. 33, pp. 3339-42, 2013-Dec 2013. </p><p>[5] W. Feng, P. Xiao, X. Feng, X. Chang, and Y. Yang, "Grassland Change Detection Based on Remote Sensing Imagery in Typical Area of Hulunbuir Grassland from 1989 to 2010," Remote Sensing Information, vol. 29, pp. 61-67, 2014 2014. </p><p>[6] J. Im, J. R. Jensen, J. A. Tullis, and Ieee, Development of a remote sensing change detection system based on Neighborhood Correlation Image analysis and intelligent knowledge-based systems, 2005. </p><p>[7] G. M. Foody, A. Mathur, C. Sanchez-Hernandez, and D. S. Boyd, "Training set size requirements for the classification of a specific class," Remote Sensing of Environment, vol. 104, pp. 1-14, Sep 15 2006. </p><p> /ColorImageDict &gt; /JPEG2000ColorACSImageDict &gt; /JPEG2000ColorImageDict &gt; /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 200 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 2.00333 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict &gt; /GrayImageDict &gt; /JPEG2000GrayACSImageDict &gt; /JPEG2000GrayImageDict &gt; /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 400 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 600 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.00167 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict &gt; /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False</p><p> /CreateJDFFile false /Description &gt; /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ &gt; /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeP...</p></li></ul>


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