Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model |
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| Authors: | Yichun Xie Zongyao Sha Yongfei Bai |
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| Institution: | 1. Eastern Michigan University, Institute for Geospatial Research & Education, 125 King Hall, Ypsilanti, MI 48197, United States;2. Wuhan University, International School of Software, Wuhan, Hubei Province, China;3. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun, Xiangshan 100093 Beijing, China;1. Computational Science Research Center at San Diego State University, 5500 Campanile Drive, San Diego, CA 92182-1245, USA;2. Faculty of Geo-information Science and Earth Observation at University of Twente, Hengelosestraat 99, 7514 AE, Enschede, The Netherlands |
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| Abstract: | Large and growing archives of orbital imagery of the earth’s surface collected over the past 40 years provide an important resource for documenting past and current land cover and environmental changes. However uses of these data are limited by the lack of coincident ground information with which either to establish discrete land cover classes or to assess the accuracy of their identification. Herein is proposed an easy-to-use model, the Tempo-Spatial Feature Evolution (T-SFE) model, designed to improve land cover classification using historical remotely sensed data and ground cover maps obtained at later times. This model intersects (1) a map of spectral classes (S-classes) of an initial time derived from the standard unsupervised ISODATA classifier with (2) a reference map of ground cover types (G-types) of a subsequent time to generate (3) a target map of overlaid patches of S-classes and G-types. This model employs the rules of Count Majority Evaluation, and Subtotal Area Evaluation that are formulated on the basis of spatial feature evolution over time to quantify spatial evolutions between the S-classes and G-types on the target map. This model then applies these quantities to assign G-types to S-classes to classify the historical images. The model is illustrated with the classification of grassland vegetation types for a basin in Inner Mongolia using 1985 Landsat TM data and 2004 vegetation map. The classification accuracy was assessed through two tests: a small set of ground sampling data in 1985, and an extracted vegetation map from the national vegetation cover data (NVCD) over the study area in 1988. Our results show that a 1985 image classification was achieved using this method with an overall accuracy of 80.6%. However, the classification accuracy depends on a proper calibration of several parameters used in the model. |
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