Satellite Imagery. Casson Stallings. ManTech Environmental Technologies, Inc. PO Box 12313. Research Triangle Park, NC 27709, USA. Siamak Khorram.
Incorporating Ancillary Data into a Logical Filter for Classified Satellite Imagery Casson Stallings ManTech Environmental Technologies, Inc. PO Box 12313 Research Triangle Park, NC 27709, USA
Siamak Khorram Center for Earth Observation Box 7106 North Carolina State University, Raleigh, NC 27695-7106, USA
Rodney L. Huffman Department of Biological and Agricultural Engineering Box 7625 North Carolina State University, Raleigh, NC 27695-7625, USA
Abstract Classified imagery is commonly post-processed before integration into a geographic information system (GIS). Post-processing often consists of a rectangular majority filter (e.g., 3 x 3 mode filter) being applied to the classified imagery. An alternate type of filter, the polygon mode or object filter, is also occasionally used in post-processing. The polygon mode filter is similar to the rectangular mode filter, except that rather than using a kernel of fixed size, the regions are determined a priori. The filter is then applied to the regions as a whole, assigning a whole region to be equal to the dominant land cover in that region. The rectangular and polygon filters both reduce the speckle and polygon count in the resulting data. The objectives of this research were to test a polygon mode filter using agricultural field boundaries that were known a priori and to compare the effects of this filter to the more commonly used rectangular filters. The polygon and rectangular mode filters all increased the land cover accuracy and reduced the polygon count. The overall classification accuracies were improved by the filters: 4% for the 3 x 3 rectangular filter, 5% for the 5 x 5 rectangular filter, and 15% for the polygon filter.
Introduction Logical Smoothing Land cover classifications derived from remotely sensed imagery inevitably have many small patches containing only a few cells. This causes the map or image to have a saltand-pepper or speckled appearance (Thomas, 1980; Townsend, 1986; Lillesand and Kiefer, 1994). These patches are often below a projects minimum mapping unit (i.e., the smallest area assigned to a category of interest) established for the project. People are seldom concerned about the classification of an image at the resolution of a single pixel (Davis and Peet, 1977). The patches can be confusing to viewers of the map (Davis and Peet, 1977), and it is often assumed that single cell patches, or elementals, are less likely to be correctly classified (Grunblatt, 1987; Lillesand 42
and Kiefer, 1994). The large number of small patches, when converted to vector format, often result in large and cumbersome data sets. Logical filters are applied to address the described problems. In a geographic context, a logical filter alters the classification of a central cell or cells based on the neighboring cells (e.g., a 3 x 3 kernel). The kernel shape and the decision rules used in the filter can vary. There are three main reasons for the use of logical filters: 1. To reduce the noise (salt-and-pepper) in the classified data, making the map more appealing and less confusing (Davis and Peet, 1977; Thomas, 1980; Townsend, 1986; Lillesand and Kiefer, 1994). 2. To decrease the number of patches in the map and therefore the number of polygons resulting when the cell-based map is vectorized (Khorram et al., 1992,
Geocarto International, Vol. 14, No. 2, June 1999 Published by Geocarto International Centre, G.P.O. Box 4122, Hong Kong.
Khorram et al., 1995a, Heather Cheshire, personal communication, 1995). 3. To increase the accuracy of the final map (Thomas, 1980; Scarpace et al., 1981; Grunblatt, 1987; Janssen et al., 1990; Wang and Civco, 1992). Majority and Mode Filters The most commonly used logical filters are rectangular majority (or mode) filters. These alter the class of a central cell based on the frequency of classes within a square region around the cell. Typically this region is somewhere between 3 x 3 and 7 x 7 cells, but can be larger. The decision rules for reassigning the central pixel vary. One typical rule is to reassign the central cell if more than half of the cells in the region, a majority, come from another class. Alternatively, the central cell can be reassigned if any class shows a clear mode (i.e., plurality). We used mode decision rules or mode filters in this study. A variety of options exist in filter implementation. Current software allows one to specify filter kernels of various geometric shapes or even to use regions specified in another grid (ERDAS, 1994; ESRI, 1994). The influence of cells within a region can be weighted based on their class or distance from a central cell (Thomas, 1980; Lillesand and Kiefer, 1994). The filter can be constrained to act only on desired cells (Lillesand and Kiefer, 1994), based on the following: (1) Patch size (Davis and Peet, 1977). (2) Containment and connectivity rules (Townsend, 1986; Wang and Civco, 1992). (3) The pixel-specific probability of misclassification (Wang and Civco, 1992). (4) Some combination of (1) through (3). Townsend (1986) found several problems with a typical 5 x 5 filter using a majority decision rule. He used test grids to show that this filter could remove substantial information from grids containing narrow patches and gave inconsistent results for identical patches oriented in different directions. He overcame these two difficulties by constraining the filter to operate only on unconnected single cell patches. This reduced the noise in the grid and can increase the overall accuracy of land cover data by 5% (Scarpace et al., 1981). Wang and Civco (1992) tested the effect of using a rectangular majority filter on selected cells within a classified image. Cells with higher probabilities of being incorrect, based on their Mahalanobis Distance in the initial classification, were selected for reclassification. The specific threshold for determining which pixels were “possibly misclassified pixels” (PMPs) was based on analyst’s knowledge of the study area (Wang and Civco, 1992). The classification of these cells was changed if they met one of the following criteria: one or two adjacent PMPs were contained within another class, or the 3 x 3 region surrounding the cell had only two classes and the other class was dominant. Post-classification of the PMPs resulted in overall accuracy improvements of 7.1% to 7.4% in their
three study areas. Davis and Peet (1977) implemented a logical smoothing filter that replaced all cells from patches below a userspecified size; the size could vary by class. Each patch was filled with the class having the most cells bounding the patch. Thomas (1980) used a filter that weighted cell influences based on their distance from the central cell. Both methods were found to reduce the image noise, but no formal accuracy assessments were done. Object Filters An object filter is similar to rectangular majority or mode filters except that it is applied to a region defined by ancillary data (e.g., agricultural field polygons). The entire region is set to the class that occurs most frequently within that region. The regions do not typically overlap. Object filters have an advantage over typical kernel based filters because they can use arbitrarily shaped and sized regions. There are two main benefits cited for object filters: (1) they increase the accuracy of the land-cover data, and (2) they condense the heterogeneous land cover data to one cover type per region (Grunblatt, 1987; Janssen et al., 1990). Grunblatt (1987) looked at how satellite image boundary blur affected the classification accuracy of agricultural fields. After classification he compared error matrices for the crops using all cells in the image with the error matrices using only those cells well within the boundary of the fields. The class-specific accuracies increased dramatically when the cells near the edges were excluded. Reassigning the cover type of whole fields to the mode class within the field resulted in 12% to 29% improvements in the classspecific accuracies. Janssen et al. (1990) defined object boundaries in a GIS. After the initial classification of land cover using typical remote-sensing techniques, the authors applied an object filter. This resulted in overall accuracy improvements of 12% and 20% in their study areas. Background Our desire to use remotely-sensed land-cover data as an input to a field based water quality model provided the initial impetus for studying the use of an object filter (Stallings et al., 1992; Khorram et al., 1995b; Stallings, 1995). Classified data typically contain considerable speckle and do not reflect this within field homogeneity. The reason for applying an object filter is that only one crop is grown on each field and an object filter returns a single crop type for each agricultural field. The effects of an object filter have not previously been compared to those of rectangular kernel filters that are commonly used on land-cover data before vectorization. Rectangular filters are also used to decrease the patch count and have been reported to improve the accuracy of final land cover data. In addition, previous studies of the rectangular and object filters have not looked at class specific accuracies to see if the improvements in overall accuracies are being achieved at the expense of specific 43
Cropping data were linked to the geographic data described previously.
classes. Objectives The purpose of this study was to quantify the effects of applying an object mode filter to land-cover data and to compare these effects to those of the standard rectangular mode filters. The specific objectives of this study were to: 1. Quantify the map accuracy improvements resulting from application of the object mode filter on small and irregular agricultural fields in North Carolina. 2. Compare the effects of the object filter to the standard rectangular filter based on (1) changes in the land-cover patch count, and (2) changes in overall and class specific accuracies.
Methods Study Site The Landsat Thematic Mapper image used covers the 2044-hectare (5050-acre) Herrings Marsh Run Watershed and some area surrounding it in Duplin County, North Carolina (Figure 1). All fields which, in 1990, had one of the major crops—soybeans, corn, cotton, tobacco, and hay— were used in this study. Fields with grains and sweet potatoes were also considered. Additional land-cover classes included water, forest, open (including hay and set aside lands), and bare or nearly bare soil. A total of 337 reference land-cover patches (or polygons) were used (Figures 2, 3). Agricultural Field Boundaries Agricultural field boundaries were derived from Farm Services Agency (FSA) aerial photography. FSA personnel drew the boundaries on 24" x 24" photos at a scale of l:7,900. The field boundaries were digitized (Stallings, 1995). Arc/Info (ESRI, 1994) was used for most of the GIS work. Errors in the field boundaries were generally less than 12 m, but were as high as 21 m (Stallings, 1995). Photo rectification and determination of boundary errors is discussed in Stallings (1995). Cropping Data The Cooperative Extension Service (CES) collected the field-specific cropping information (Coffey et al., in press). The CES tried to survey each farmer in the watershed. The survey contained questions on cropping, planting and harvesting dates, and many additional farm practices.
Figure 1 Carolina. 44
The box shows the image area, in Duplin County North
Additional Land-Cover Classes Several land-cover classes were added to the agricultural fields coverage for use in classifying the satellite image. We determined the locations of forest and scrub wetlands using a 1:24,000 National Wetlands Inventory (NWI) map. The locations of upland forests were determined from FSA aerial photography. Upland forest areas were segregated into four categories based on stand density. All forest classes were combined in the final classified image. Ponds and visible channels in the study area were determined from the USGS 7.5 quadrangle and from a false color composite of the satellite image. Agricultural fields dominated by bare soil, where the crops did not yet provide significant cover, were determined by visual classification of the image after a tassled cap transformation (Crist and Cicone, 1984; Crist and Knauth, 1986). These land cover data sources were merged, resulting in one polygon coverage in which each polygon had a defined class. This coverage was then used as the reference land cover data (Figure 2). About 15% of the study area had defined reference land cover classes. Satellite Image Rectification and Classification The Landsat TM scene was chosen to provide maximum discrimination between the major crops in the study area. A search was done to identify scenes with minimal cloud cover that were available for the 1990 growing season over the study area (Landsat path 15, row 36). We selected the July 11, 1990 scene based on the planting, harvesting, and expected leaf area index (LAI) of corn, cotton, soybeans, and tobacco. All image processing was done in ERDAS IMAGINE (1994). The transformation equation for rectification was created using 20 ground control points. The two ground control points with the worst RMS deviations were eliminated. The remaining 18 were used to define a new transformation matrix that was applied to the image, converting it to North Carolina State Plane coordinates (RMS error of 25.9 m or 0.908 pixels). In defining training sites, it was desired to represent as much spectral variation in the image as possible. The 28 land-cover classes defined in the reference data were used for training classes (Table 1). An iterative supervised classification scheme was used. Several training sites were chosen from each of the training classes. Polygons to be used for training sites were generally chosen at random. Random selection was not possible for classes that contained too few polygons; for these classes polygons were chosen manually, attempting to account for all of the spectral variation within the class while maintaining a good geographic distribution. A region growing algorithm (i.e., the SEED function in IMAGINE) was used to select pixels within the polygons to be used as the training sites. Multiple training sites were
Figure 2
Table 1
Land cover reference patches (polygons) in the image area. The Herrings Marsh Run Watershed is shown in blue. The legend for this and other land cover images is shown in Figure 3.
Land-cover classes used for the training site selection and reference data. 1
Final classes Water (17) Forest (99) Open (35) Soil (67) Agriculture (132) 1
Training classes Water-riverine, water-pond Wetland forest (4 types), wetland scrub (4 types), upland forest (4 types) Open, pasture, fescue grass, Bermuda grass, conservation reserve program Soil Corn, cotton, soybeans, tobacco, millet, sweet potatoes
The number of reference polygons is given in parenthesis.
derived from within a single polygon when a class had only a few reference polygons and there was sufficient spectral variation within a polygon. This resulted in better representation of the spectral variation for these small classes. There were a few cases where the region-growing algorithm was not used. The training sites for water classes were delineated by hand while viewing the image. Some of these sites were distinct within the image and also so small that slight misregistrations between the map and image would cause great errors in the training class means. For the wetland scrub class, all pixels within the randomly selected polygons were used because the polygons were so small. Wet forest training classes were defined by performing a cluster analysis on the pixels within seven wet forest polygons. This produced eight additional training classes. Training classes were merged or eliminated if they were redundant as shown by ellipse plots and cross-classification of the training data. The image was classified twice using a maximum likelihood classifier to test the signatures. Each time, training classes that created obvious problems were eliminated. The
Figure 3
Land cover legend for all images.
image was classified a final time with the remaining training classes. The 28 classes were merged resulting in the five class scheme (Table 1, Figure 4). The work described was also done on a ten class scheme (Stallings, 1995); the results were similar, but are not discussed here. Post-Classification Filtering The classified grid was transferred to Arc/Info and filtered using three methods: a 3 x 3 mode filter (Figure 5); a 5 x 5 mode filter (Figure 6); and an object mode filter (Figure 7). The 3 x 3 and 5 x 5 rectangular filters assigned the dominant class to the central pixel. The object filter assigned the dominant class to the entire field patch. In each case there may have been up to five separate classes represented within the filter area. Since a mode or plurality rule was used, there was no requirement that more than half of the pixels be in a single class before any were reclassified. Accuracy Assessment The accuracy assessment was carried out in Arc/Info and Splus (Statistical Sciences, 1993). The training sites were transferred from ERDAS to Arc/Info. An error matrix was derived for each map: the original classification and three filtered maps. The 250 reference polygons were used to identify the true class. The areas of the reference data not used in developing training sites were all sampled for the error matrix (Stallings, 1995). The overall accuracy, users and producers accuracy, and Khat were calculated for each data set for all classes (Cohen, 1960; Aronoff, 1982ab; Congalton et al., 1983; Hudson and Ramm, 1987).
Results Classification Accuracy The overall accuracy (77.0%) and Khat (0.632) of the 45
Figure 4
The original classification containing five land cover classes.
Figure 5
The classified image after the application of a 3 x 3 rectangular mode filter.
Figure 6
The classified image after the application of a 5 x 5 rectangular mode filter.
Figure 7
The classified image after the application of the polygon mode filter.
original classification were poor. Khat ranges from 0; indicating only random correlation, to 1, indicating a perfect correlation between the reference and classified data. The class specific accuracies ranged from 20% to about 100%. These are discussed more in a following section. The low correlation was not entirely unexpected. The study area consists of many small fields, only one image was used where multi-temporal images would have been preferred (Schreier et al., 1982; Badhwar et al., 1984), and there was little choice of image dates. Hay (1982) noted that specific crops can often only be discriminated spectrally at certain stages of development. There is not necessarily a single
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time during the growing season that all crops can be discriminated spectrally. The Effect of Filtering on Patch Count and Visual Appearance Each filter had a dramatic effect on the patch count (Figure 8) and visual appearance of the classified image (Figures 5–7, 9). The 3 x 3 mode filter eliminated single cells and other small patches, decreasing the patch count substantially. The 5 x 5 mode filter eliminated more and larger regions. The object filter decreased the patch count to the number of reference patches. Since the object filter
only operates on the specified areas, areas outside the reference patches (85% of the study area) were left undefined. In addition to eliminating patches, the rectangular mode filters also smoothed the edges of larger patches. These two generalization actions together decreased the speckle and heterogeneity of the images. This gives the images a more homogenous look, allowing one to better see and interpret the larger features.
1800 1600 1400 1200
Patch Count
1000 800 600 400 200 0
Original
3x3
5x5
Polygon
Filter Applied Figure 8
The number of land cover patches in the original classification and after the application of 3 x 3, 5 x 5, and polygon mode filters.
Figure 9
The Effect of Filtering on Accuracy Each of the filters increased the overall and Khat accuracies of the classifications (Figure 10). The rectangular filters increased the overall accuracies by 3.5% (3 x 3) and 5.1% (5 x 5). The object filter led to a more substantial increase of 14.0%. The individual class accuracies were improved by filtering in almost every case (Figures 11 and 12). Generally, the largest improvements resulted from the larger filters (polygon > 5 x 5 > 3 x 3 > unfiltered). Only occasionally, in the case of
A closeup of the four images showing the differences caused by the three filter types. 47
those cells used to derive spectral training classes. This should give the best accuracy measures possible for the regions of interest. A typical random or stratified sampling of the grid was undesirable in this study because it could not be used to calculate the accuracy of the grid after the object filter is applied. A random sample of the filtered maps would look at different populations since 85% of the map is undefined after application of the object filter. Application of any type of filter can cause a loss or change in the information content of an image. This loss may cause the image to be less suitable for certain types of analysis. For example, the object filter removes all information on intra-object variability, which might be useful in identifying areas of low crop production within fields.
Figure 10
Increase in overall accuracy and Khat (as Khat *100) for each filter type.
water and forest, did the filtering decrease the class specific accuracies. The water patches were very small and the rectangular filters eliminated some completely.
Discussion The accuracy improvements found for the filters are similar to those found in previous studies. Earlier studies (Scarpace et al., 1981; Wang and Civco, 1992) found that rectangular filters improved the overall accuracy by 5% to 7%, whereas this study found improvements of 3% to 5%. Earlier studies of the object filter (Grunblatt, 1987; Janssen et al., 1990) found improvements of 12% to 30% whereas this study showed an improvement of 14%. The object filter consistently improved the class specific accuracies. These class-specific improvements were not made at the expense of accuracies in another class since all classes showed improvements. The rectangular filters usually improved the user’s and producer’s accuracies. By contrast though, there were cases where the rectangular filters decreased the accuracy of specific classes, probably by removing small, but correctly classified, patches. The 5 x 5 filter generally decreased the accuracy more for these classes, demonstrating what Townsend (1986) found, that larger rectangular filters have the capability of destroying more information. Accuracy Assessment The accuracy assessment for this study did not utilize the traditional random or stratified sampling. Rather, the population of interest was considered to be the defined reference polygons. These areas were the only ones that had a defined land cover after the application of all filters. The reference polygons were sampled completely except for 48
Limitations of This Study Earlier studies have indicated that classification errors are more prevalent near edges (Devine and Styron, 1994; Grunblatt, 1987). Unpublished work by the authors has confirmed that this is also true for the data in the current study. Three reasons have been cited for the increased errors near edges: (l) mixed pixels, (2) sensor lag time, and (3) spatial inconsistencies between the classified and reference data. Classification errors near the patch edges indicate that an alternate technique for determining the cell frequency may improve the object filter. Basing the cell frequencies on the interior cells (e.g., those cells two cells away from an edge) should improve the polygon mode filters performance. Grunblatt’s (1987) work showed that cells two pixels away from the boundary were generally classified about 20% better than those on the boundary. This is probably not feasible for some of the patches in this study area since they are so small. One possible alternative is to use a weighting scheme (similar to that used by Thomas, 1980) where the cells close to the boundary are considered less important in the frequency calculation. The spatial accuracy of the fields data is between 4 and 21 m depending on the error estimation technique (Stallings, 1995). The spatial error in the satellite imagery is less than 28.5 m (one pixel). Visual assessment of the correspondence between the fields coverage and the satellite imagery indicate that most discrepancies were less than one pixel, although some were up to two pixels. Small spatial inconsistencies may make the object filter appear to work better than it does. Model Generalization A related study by Stallings (1995) indicated that the effect of the filters varied depending on the initial classification accuracies. The filters can also change the frequency of classes within the map. This effect is dependent on the heterogeneity of the map and on the typical patch sizes of each class. These effects need to be considered, and perhaps better quantified, when these filters are used as model generalization functions for land-cover data. Each filter has desirable effects as a model generalization tool for land-cover data. They increase the overall accuracy
Figure 11
User’s accuracies for the original image and each filtered image.
Figure 12
Producer’s accuracies for the original image and each filtered image.
and typically the class accuracies of the data. This is unique as generalization functions go, since generalization functions almost always increase errors in the data (Stallings et al., 1995). The filters also tend to maintain the larger and dominant features in the landscape. This is almost always appropriate for cartographic generalization and in most circumstances will probably be appropriate for model generalization. Future Work The fact that the filters increase the image accuracy indirectly indicates that elemental cells and small groups of cells tend to be wrong. It would be valuable to directly test whether or not elemental cells tend to be wrong. If true, it would be another technique that could be used to identify PMPs. This technique is spatial and not spectral in nature, and therefore might be a good complement to the
Mahalanobis Distance and a posteriori probabilities of classification which are currently used to identify PMPs (Wang and Civco, 1992; Corves and Place, 1994; Foody, 1994). The logical filters decreased the heterogeneity of the mapped land cover within agricultural fields. If one is looking at a phenomena that utilizes measures of heterogeneity, (e.g., crop condition; Waddington and Lamb (1990) and Thenkabail et al. (1991)) the use of the logical filters would be inappropriate. The filters, especially the object filter, decrease the heterogeneity within the fields and decrease the information that might be gained.
Conclusions One of the driving forces of this project was the desire to
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decrease the number of patches in the land-cover grid before integration into a vector-based polygon coverage. All filters substantially decreased the patch count and heterogeneity of the land-cover grids. The bigger the filter, the greater the decrease in the patch counts. The object mode filter, by design, decreased the patch count within each field to one. This is conceptually desirable for agricultural fields since typically only one crop is grown on a field at any point in time. Each filter increased the overall accuracy and Khat of the grids. The increase was greatest for the object filter. This is probably because the object filter includes additional information, and was able to use larger areas of the grid to derive the final land-cover class. The object filter also improved the class specific user’s and producer’s accuracies of all classes simultaneously. The rectangular filters usually improved the user’s and producer’s accuracies. The object filter is a beneficial function for postprocessing land-cover maps and is superior in several ways to rectangular mode filters. However, it requires additional information (compared to the rectangular filters) which may not always be available, especially for large areas. It may also only be applicable in limited situations, those where boundaries remain constant, but the landcover changes with time. If the boundaries change from time to time, then they will have to be re-digitized and much of the efficiency of the technique is lost. Agriculture and forestry are the most obvious applications. Where applicable, the object filter can result in substantial improvements in land-cover accuracy.
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