Strategies for Integrating Information from Multiple Spatial Resolutions into Land-Use/Land-Cover Classification Routines

With the development of new remote sensing systems, veryhigh spatial and spectral resolution images now provide a source for detailed and continuous sampling of the Earth’s surface from local to regional scales. This paper presents three strategies for selecting and integrating information from different spatial resolutions into classification routines. One strategy is to combine layers of images of varying resolution. A second strategy involves comparing the a posteriori probabilities of each class at different resolutions. Another strategy is based on a top-down approach starting with the coarsest resolution. The multiresolution strategies are tested using simulated multiresolution images for a portion of the rural-urban fringe of the San Diego Metropolitan Area. The classification accuracy obtained from using three multiple strategies was greater when compared with that from a conventional single-resolution approach. Among the three strategies, the top-down approach resulted in the highest classification accuracy with a Kappa value of 0.648, compared to a Kappa of 0.566 for the conventional classifier.

[1]  Peng Gong,et al.  An assessment of some factors influencing multispectral land-cover classification , 1990 .

[2]  Darrel L. Williams,et al.  Performance comparisons between information extraction techniques using variable spatial resolution data , 1985 .

[3]  Danielle J. Marceau,et al.  Remote sensing and the measurement of geographical entities in a forested environment. 1. The scale and spatial aggregation problem , 1994 .

[4]  D. Stow,et al.  Multi-Resolution Classification Framework for Improving Land Use/Cover Mapping , 2002 .

[5]  Darrel L. Williams,et al.  The effects of spatial resolution on the classification of Thematic Mapper data , 1985 .

[6]  Alan H. Strahler,et al.  On the nature of models in remote sensing , 1986 .

[7]  J. Cihlar.  Land cover mapping of large areas from satellites: Status and research priorities , 2000 .

[8]  F. Wang.  Improving remote sensing image analysis through fuzzy information representation , 1990 .

[9]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[10]  R. Fournier,et al.  Remote sensing and the measurement of geographical entities in a forested environment. 2. The optimal spatial resolution , 1994 .

[11]  S. Barr,et al.  INFERRING URBAN LAND USE FROM SATELLITE SENSOR IMAGES USING KERNEL-BASED SPATIAL RECLASSIFICATION , 1996 .

[12]  Robert M. Gray,et al.  Multiresolution image classification by hierarchical modeling with two-dimensional hidden Markov models , 2000, IEEE Trans. Inf. Theory.

[13]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[14]  C. Woodcock,et al.  The factor of scale in remote sensing , 1987 .

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

[16]  Giles M. Foody,et al.  Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data , 1996 .

[17]  Mark A. Friedl,et al.  Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE , 1995 .

[18]  D. Toll,et al.  Effect of Landsat thematic mapper sensor parameters on land cover classification , 1985 .

[19]  Curtis E. Woodcock,et al.  Nested-hierarchical scene models and image segmentation , 1992 .

[20]  Philip J. Howarth,et al.  Multispectral Classification of Land Use at the Rural-Urban Fringe Using Spot Data , 1988 .

[21]  D. Rokos,et al.  The integration of spatial context information in an experimental knowledge-based system and the supervised relaxation algorithm : two successful approaches to improving SPOT-XS classification , 1996 .

[22]  P. Gong,et al.  Frequency-based contextual classification and gray-level vector reduction for land-use identification , 1992 .

[23]  Lee de Cola,et al.  Simulating and Mapping Spatial Complexity Using Multi-Scale Techniques , 1994, Int. J. Geogr. Inf. Sci..

[24]  T. Ranchin,et al.  Mapping of Urban Areas: A Multiresolution Modeling Approach for Semi-Automatic Extraction of Streets , 2000 .

[25]  Lee De Cola,et al.  Multiresolution convariation among landsat and AVHRR vegetation indices , 1997 .

[26]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[27]  D. Stow,et al.  THE EFFECT OF TRAINING STRATEGIES ON SUPERVISED CLASSIFICATION AT DIFFERENT SPATIAL RESOLUTIONS , 2002 .

[28]  Peter M. Atkinson,et al.  Choosing an appropriate spatial resolution for remote sensing investigations , 1997 .

[29]  C. Hlavka,et al.  Statistical models of fragmented land cover and the effect of coarse spatial resolution on the estimation of area with satellite sensor imagery , 1997 .

[30]  Philip J. Howarth,et al.  The Effects of Spatial Resolution on Land Cover/Land Use Theme Extraction from Airborne Digital Data , 1987 .

[31]  Daniel L. Civco,et al.  Artificial Neural Networks for Land-Cover Classification and Mapping , 1993, Int. J. Geogr. Inf. Sci..