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An unbiased sampling framework for ground data collection in digital image classification
Ranjith Premalal De Silva Dayawansa N.D.K., Department of Agricultural Engineering, University of Peradeniya, Peradeniya, SRI LANKA. J.C. Taylor Cranfield University, Silsoe, Bedford MK45 4DT England.
Introduction
Ground data collection is an integral part of the classification process where relationships are sought to relate spectral signatures of a satellite image with the corresponding ground features. In addition to the task of training spectral signatures, ground data collection through a field survey provides the base data for the determination of accuracy of supervised classification, assessment of the classification generated by unsupervised classification algorithms and identification of empirical relationships between surface properties and satellite observations. Hence, the collection of an unbiased data set for ground information is imperative for a successful image classification and subsequent analysis. This study focuses on such a methodology and its application for land use/ cover assessment in the Upper Mahaweli Catchment (UMC), Sri Lanka. Image Pre-Processing The majority of the UMC was covered by single quadrant (path 21, row 64, top left quadrant of the I2164A1) of the IRS LISS II image. However, four quadrants were required to produce the mosaic for entire UMC coverage. The four image quadrants were geometrically corrected individually using the first order polynomial transformations. However, before generating the mosaic for the catchment, each band of the smaller mosaic covering the western strip of the UMC was radiometrically corrected to adjust the relative brightness values using the linear transformation functions. The Optimum Index Factor (OIF) based on the variance and the correlation among different bands was used to identify the best three band combination. Interestingly, the commonly used standard False Colour Composite (FCC) from bands 2, 3 and 4 derived from IRS ranks second according to OIF definition while bands 1,3 and 4 were found to be the best combination. Histogram equalization for non-linear contrast stretch was applied to the image to obtain an improved visible and digital contrast stretch of the image. Statistical Sampling Methodology A methodology based on area-frame sampling was adopted for this study (Taylor & Eva, 1992). Unaligned systematic random samples were chosen with 1 sq. km. fixed size ground segments. A 10 km. grid corresponding to 1:10000 map sheets was overlaid upon the image and the locations of ground segments were chosen randomly from each grid square of 10 sq. km.. The total number of ground segments was 38 and it produced a sampling frequency of 1.22% of the 3122 sq.km. study area. Stratification of the catchment was not required as there was no strong basis for defining regional strata in UMC. The location of ground segments is shown in Fig. 01.
Field Data Formats and Documentation A cubic convolution based resampling produced 20 m resolution and resulted in an array of 50 rows and columns of data for each segment. A 400 m boundary was allowed to make the recognition of ground features easy and comparable with each image segments. Further, a 5 km. x 5 km. area surrounding the ground segment was also printed out to obtain a synoptic view of the area and ground features. It also helped in field navigation. In addition to the image segment, a transparent overlay of the available map sheet was also produced at 1:10000 scale. The enlarged overlays of land use maps at 1:50000 and 1:63360 provided the information required to locate the ground segments and to calculate relative distances between ground features whenever necessary. A proforma was prepared to record field data corresponding to the features on the image segment. Another transparency overlay was used to draw the field parcel boundaries and record the spatial extent of land use on the image segment. A Trimble Geoexplorer GPS fitted with an external antenna provided the location information for navigation in the field. The land use and contour maps of 1:10000, 1:50000 and 1:63,360 were also used for route planning for the field visits. Area Estimation by Direct Expansion Digitizing of ground data segments was repeated in an attempt to minimize errors. It also provides the direct expansion estimates that quantify the land use status of an area through the statistically valid sampling procedure. In this study, these estimates were not intended to provide crop area statistics in terms of the coverage. However, these provided the a priori weightings, used later in the digital classification procedure (Swain & Davis, 1978). Unsupervised Classification Initially, the sequential clustering was employed to identify 50 classes and corresponding spectral signatures from the image multi spectral space. The mean and standard deviation of DNs belonging to each class were calculated individually. The calculated class statistics was subject to Hierarchical Cluster Analysis using the Ward method. The basis for using this algorithm was to produce a lower number of classes through a process of agglomeration from a large population of classes minimizing the variance within each spectral class and while maximizing the variance among different classes (Hung, 1994; Richards, 1986). The algorithm tries to find the optimal spectral class combinations for the number of user-specified classes and results in a tree structure that is shown as a fusion dendogram. In this exercise, the final number of spectral classes was defined to be 10 so that it could be directly comparable with the scheme developed for the supervised classification. The spectral signatures produced for 50 classes were regrouped according to the combination given in the fusion dendogram in order to obtain the final 10 signatures, which were assumed to be corresponding to the defined classification scheme. Mosaicing of Ground Data Segments A mosaiced image was created by extracting all the ground segments using a C++ program from the image covering UMC. The collection of imagettes into a single image (QUILT) was useful in identifying and evaluating training data for the maximum likelihood classifier and also in assessing the accuracy of classification.
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