Object-oriented method for urban vegetation mapping using IKONOS imagery

Urban vegetation plays an important role in quality of life. However, accurate urban vegetation maps cannot be easily acquired from multispectral remotely sensed data alone because the spectral bands are indistinct among different vegetation classes. This study aimed to detect urban vegetation categories from IKONOS imagery based on an object-oriented method that can integrate both spectral and spatial information of objects in the classification procedure and thus can improve classification capability. Considering the characteristics of urban vegetation in IKONOS imagery, a two-scale segmentation procedure was designed to obtain ‘objects’, and the feature set for vegetation objects was constructed. Redundant information among the features was then removed by using correlation analysis, the Jeffries–Matusita (J–M) distance and principal component transformation (PCT). Finally, the vegetation objects were identified by the classification and regression tree (CART) model. The results show that IKONOS imagery can be used to map vegetation types with a total accuracy of 87.71%. Segmentations involving both micro and macro scales could acquire better vegetation objects than using a single scale. The correlation analysis combined with the J–M distance and PCT was efficient in optimizing the feature set. The rule-based classification method is suitable for identifying urban vegetation types using the feature set with a complex structure.

[1]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[2]  Huang Hao.  A Shadow Automated Extraction Method of IKONOS Images Based on Image Fusion , 2004 .

[3]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[4]  C. M. Lee,et al.  Urban vegetation monitoring in Hong Kong using high resolution multispectral images , 2005 .

[5]  A. Huete,et al.  MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data , 1999 .

[6]  M. Baatz,et al.  Object-oriented and Multi-scale Image Analysis in Semantic Networks Introduction: the Necessity for Integration of Remote Sensing and Gis , 2022 .

[7]  J. P. Marques de Sá,et al.  Pattern Recognition: Concepts, Methods and Applications , 2001 .

[8]  Curt H. Davis,et al.  A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

[9]  Geoffrey J. McLachlan,et al.  Discriminant Analysis and Statistical Pattern Recognition: McLachlan/Discriminant Analysis & Pattern Recog , 2005 .

[10]  Gu Guo-qin.  Study on extraction of urban green space from IKONOS remote sensing images , 2004 .

[11]  Tian Qing-jiu.  A Shadow Detection and Removal Method for IKONOS Images , 2004 .

[12]  Zhang Xiangmin,et al.  Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China , 2006 .

[13]  P. Mather,et al.  Classification Methods for Remotely Sensed Data , 2001 .

[14]  P. Chavez.  An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

[15]  A. Kimerling,et al.  A per-segment approach to improving aspen mapping from high-resolution remote sensing imagery , 2003 .

[16]  Prasad S. Thenkabail,et al.  Inter-sensor relationships between IKONOS and Landsat-7 ETM+ NDVI data in three ecoregions of Africa , 2004 .

[17]  P. Gong,et al.  Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .

[18]  D. Landgrebe,et al.  The K-L Expansion as an Effective Feature Ordering Technique for Limited Training Sample Size , 1983, IEEE Transactions on Geoscience and Remote Sensing.

[19]  R. Mathieu,et al.  Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery , 2007 .

[20]  Volker Walter,et al.  Object-based classification of remote sensing data for change detection , 2004 .

[21]  J. Schiewe,et al.  Potential and problems of multi-scale segmentation methods in remote sensing , 2001 .

[22]  Curt H. Davis,et al.  A fuzzy classification approach for high-resolution multispectral data over urban areas , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[23]  Soo Chin Liew,et al.  Texture retrieval using grey-level co-occurrence matrix for Ikonos panchromatic images of earthquake in java 2006 , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[24]  Adrian Doloca.  Feature selection for texture analysis using genetic algorithms , 2000, Int. J. Comput. Math..

[25]  Guo Jian-hong.  Study on Multispectral Detecting Shadow Areas and A Theoretical Model of Removing Shadows from Remote Sensing Images , 2006 .

[26]  Y. Zhang,et al.  A new merging method and its spectral and spatial effects , 1999 .

[27]  Marina Mueller,et al.  Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery , 2004, Pattern Recognit..

[28]  Ben Gorte,et al.  A method for object-oriented land cover classification combining Landsat TM data and aerial photographs , 2003 .

[29]  Jon Atli Benediktsson,et al.  Classification and feature extraction for remote sensing images from urban areas based on morphological transformations , 2003, IEEE Trans. Geosci. Remote. Sens..

[30]  Frieke Van Coillie,et al.  Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium , 2007 .

[31]  康夫 高橋.  Studies on Analysis Method of Remotely Sensed Data for Fresh Water Area and Application to Biwako Lake , 1990 .

[32]  R. Kettig,et al.  Classification of Multispectral Image Data by Extraction and Classification of Homogeneous Objects , 1976, IEEE Transactions on Geoscience Electronics.

[33]  Josef Strobl,et al.  What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS , 2001 .

[34]  Jay Gao,et al.  Quantification of grassland properties: how it can benefit from geoinformatic technologies? , 2006 .

[35]  Geoffrey J. Hay,et al.  An object-specific image-texture analysis of H-resolution forest imagery☆ , 1996 .

[36]  Peng Gong,et al.  Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery , 2004 .

[37]  Philip H. Swain,et al.  Remote Sensing: The Quantitative Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.