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

Authors:
Wei Su;Chao Zhang;Xiang Zhu;Daoliang Li
Affiliations:
College of Information #x0026/ Electrical Engineering, China Agricultural University, Beijing;College of Information #x0026/ Electrical Engineering, China Agricultural University, Beijing;College of Information #x0026/ Electrical Engineering, China Agricultural University, Beijing;College of Information #x0026/ Electrical Engineering, China Agricultural University, Beijing
Venue:
WSEAS Transactions on Information Science and Applications
Year:
2009

Citing 3
Cited 3

Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery

International Journal of Remote Sensing
An object-oriented approach for analysing and characterizing urban landscape at the parcel level

International Journal of Remote Sensing
Object oriented implementation monitoring method of zone feature in land consolidation engineering using SPOT 5 imagery

WSEAS Transactions on Computers

Classification of wetland from TM imageries based on decision tree

WSEAS Transactions on Information Science and Applications
Classification of wetland from TM imageries based on decision tree

WSEAS Transactions on Information Science and Applications
Original paper: The vegetation classification in coal mine overburden dump using canopy spectral reflectance

Computers and Electronics in Agriculture

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Abstract

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.