A hierarchical object oriented method for land cover classification of SPOT 5 imagery

Land cover classification with a high accuracy is necessary, especially in waste dump area, accurate land cover information is very important to eco-environment research, vegetation condition study and soil recovery destination. Funded by the international cooperation project Novel Indicator Technologies for Minesite Rehabilitation and sustainable development, a hierarchical object oriented land cover classification is produced in this study. The ample spectral information, textural information, structure and shape information of high resolution SPOT 5 imagery are used synthetically in this method. There are two steps in object oriented information extraction: image segmentation and classification. First, the image is segmented using chessboard segmentation and multi-resolution segmentation method. Second, NDVI is used to distinguish vegetation and non-vegetation; vegetation is classified as high density vegetation, middling density vegetation and low density vegetation using spectral information, object oriented image texture analysis; non-vegetation is classified as vacant land and main road using length/width. Accuracy assessment indicate that this hierarchical method can be used to do land cover classification in waste dump area, the total accuracy increases to 86.53%, and Kappa coefficient increases to 0.7907.

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

[2]  A. Troy,et al.  An object‐oriented approach for analysing and characterizing urban landscape at the parcel level , 2008 .

[3]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[4]  A. Lobo,et al.  Classification of Mediterranean crops with multisensor data: per-pixel versus per-object statistics and image segmentation , 1996 .

[5]  Anne Puissant,et al.  The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery , 2005 .

[6]  Manfred Ehlers,et al.  Photogrammetric Engineering and Remote Sensing , 2007 .

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

[8]  Mohan M. Trivedi,et al.  Segmentation of a high-resolution urban scene using texture operators , 1984, Comput. Vis. Graph. Image Process..

[9]  LI Dao-liang.  A Plant Species Selection Model for Revegetation of Abandoned Land Contaminated from Coal Mining Activities , 2005 .

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

[11]  Chad Hendrix,et al.  A Comparison of Urban Mapping Methods Using High-Resolution Digital Imagery , 2003 .

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

[13]  Alistair Lamb,et al.  Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas , 2009 .

[14]  Jams L. Cushnie.  The interactive effect of spatial resolution and degree of internal variability within land-cover types on classification accuracies , 1987 .

[15]  Wei Su,et al.  Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery , 2008 .

[16]  Eufemia Tarantino,et al.  Accuracy assessment of per-field classification integrating very fine spatial resolution satellite sensors imagery with topographic data , 2001 .

[17]  Stephen Wharton,et al.  A Spectral-Knowledge-Based Approach for Urban Land-Cover Discrmination , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Peng Gong,et al.  A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data , 1992 .

[19]  R. Kettig.  Computer classification of remotely sensed multispectral image data by extraction and classification of homogeneous objects. , 1975 .

[20]  Chao Zhang,et al.  Object oriented implementation monitoring method of zone feature in land consolidation engineering using SPOT 5 imagery , 2008 .

[21]  Ming-Hseng Tseng,et al.  A genetic algorithm rule-based approach for land-cover classification , 2008 .

[22]  Craig A. Coburn,et al.  A multiscale texture analysis procedure for improved forest stand classification , 2004 .

[23]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .