Can DEM Enhance The Digital Image Classification?
Apisit Eiumnoh and Rajendra P. Shrestha
STAR Program, Asian Institute of Technology
P.O. Box 4, Klong Luang, pathumthani 12120, Thailand
Tel: +66-2-5245588, 5245584; Fax: +66-2-5245597
Email :apisit@ait.ac.th
Abstract
Improving the image classification result has always been a concern while working with satellite data. Various techniques of image preprocessing, classification schemes and integration of ancillary data have shown positive results to improve the classification results. This study carried out to explore the use of Digital Elevation Model (DEM ) in Landsat -TM data classification for a watershed area of Thailand demonstrates the comparison of classification results from with or with out DEM. In our study, DEM helped to improve the classification accuracy from 58 to as high as 78 percent. The study concludes that besides band ratios and Principal Component Analysis, DEM is very helpful to improve the classification results, however there lies an important consideration that the selection of other input bands for classification be carefully considered.
Introduction
Satellite remote sensing has become a vital tool for the monitoring and management of natural resources and for environment monitoring. Improving classification accuracy of digital data has always been an important concern to extract the real world situation in the form of thematic maps. In machine classification, spectral value represented as digital number are grouped together to form certain classes to which a theme is assigned to produce a thematic map. One of the main problems when generating land cover maps from digital images is the confusion of spectral responses. The possibilities, that two or more different features having the same spectral behavior are eventually classified as the same class, not only creates the difficulties in extracting the valid information but also introduces the errors in the classification. Hence, the classification accuracy is influenced by the type of image and consequently the spatial and spectral resolutions, the spatial variability of the land cover types and the attributes to be determined among other factors (Campbell, 1987; congaltion, 1988).
Attempts have been made to improve the accuracy of image classification based on various approaches, such as use of multi-temporal imagery to individualize information classes (Conese and Maseli, 1991), piecewise linear classifier with simple post-processing (Kai-Yi and Mausel, 1993), GIS based methodology with ancillary information like soils, topography, bio-climates (Gastellu-Etchegorry et al, 1993), GIS rules with ancillary data on terrain mapping units, elevation data(Palacio-prieto and Luna-gonzalez, 1996) and so on.
Digital Elevation Model (DEM) can be created from either sterepairs derived from satellite data or aerial photographs or generated from digital terrain elevation data. DEM can be readily combined with image data (both optical and SAR) for a number of different purposes (Lillesand and Kiefer, 1987). Fore example, DEM was used to calibrate and geocode SAR data classification is very limited due to higher cost involved in preparing the DEM.
This study attempts to explore the usefulness of DEM in digital image classification combining it as a component band with various preprocessed band combinations of Landsat-TM image for the land cover classification.
The Study Area
The study area, Sakae krang river basin, is situated between 15o03' to 16o 05' N latitude and 99o 07' to 100o 04' E longitude in the central region of Thailand. The annual mean minimum and maximum temperature in the area range from 19.5o C to 33.6o C with an annual precipitation range of 950 to 1500 mm. The flat to gentle slope topography extends from east towards west which end up as a mountainous parts with more than 50 percent slope gradient forming the head watershed of the basin. The alluvial fan of the area is mostly under agriculture with the major corps, such as both irrigated and rained paddy, sugarcane, maize, orchards, plantation corps. Both mixed deciduous and dry Ditperocarp forest exists on the lower altitude limestone hills and dry evergreen forest in the mountainous area. The elevation of the area ranges from 20 to 1,641 m above mean sea level.
Materials and Methods
Materials
The primary data used in the study was the Landsat -TM data acquired for path 130/Row 49 on 21 February 1995. However, other maps, such as land use map of 1993 (1:250,000), topographic map (1:50,000) were also used for the study. Field based information gathered during the ground survey of the area was also used during training area selection and results verification. PC ARC/INFO was used to prepare the boundary of the study area and encoding elevation data to create the DEM where as image processing was done using ERDAS ver 7.5.
Image Preprocessing
Preprocessing, which is designed to remove any undesirable image characteristics produced by the sensor, refers to the initial processing of the raw data to calibrate the image radiometry, remove noise and correct geometric distortions (Schowengerdt, 1983).
The bulk format of TM data was acquired on two different CCTs which were downloaded as two different scenes. Radiometric calibration of the two different images was performed by comparing the Gray Level Histograms (GLH) of each image and sample pixel's spectral value for identified feature so that the same feature in two different images have the same spectral value.
Image noise is any unwanted signal or disturbances in an image. It can be grouped as random, isolated, stationary and non-stationary periodic noise. For the given image, few bad lines as an isolated noise were suppressed by identifying the horizontal bad lines during visual inspection and replacing them with the average value of adjacent two lines.
The geometric correction of the image was performed registering the image to 1:50,00 scale topo map sheets by selecting 45 Ground Control Points (GCPs). The Root Mean Square (RMS) error accepted was less than 1 pixel (30 m) at the first order and nearest neighborhood transformation. Two geometrically corrected scenes were then stitched together from which only the basin are was clipped with a vector layer.
Of the data reduction technique, band ratios, NDVI of NIR and R bands, and Principal Component Analysis were carried out to enhance the image as the quantity of information carried by satellite data is not necessarily same as the amount of data.
