With rapid development of the social economy and the urbanization process

building land has increased continuously

promoting the use of remote sensing images on built-up area extraction

which lays the foundation for detecting buildings and obtaining indices

such as building density and population density. Variogram is proven to be an effective methodology in extracting texture characterization with high precision when applied to urban built-up area extraction from high-resolution synthetic aperture radar (SAR) images. However

limited by factors

such as imagery resolution and structural features of rural built-up areas

the traditional way of executing the variogram is characterized by a high false alarm rate when extracting rural built-up areas in a large extent from middle- and high-resolution SAR images. Aimed at extracting rural built-up areas precisely

ensuring a high detection rate

and lowering the false alarm rate

this study proposed a new method to determine the threshold based on the iterative -parameter. Through the establishment of the brightness threshold

pixels that meet the requirement can be assigned a weight to increase the value of the variogram for pixels up to a standard in four directions (which can be considered built-up areas) and to restrain the increase of pixels only fitting one direction or having no direction (which can be considered non-built-up areas). Thus

the confusion between built-up areas and its counterpart is reduced. The experiment employed Radarsat-2 image as data source. According to the features of the variogram

cross-polarization images

VH and HV bands

and their principal component analysis transformation

which are characterized by a high discrepancy between built-up areas and non-built-up areas

are selected as input data. The first step is to determine the threshold and weight based on the iterative -parameter. The second step is to extract the feature vectors of the image using the variogram. The third step is binarization with the FCM classifier. The final step is to determine whether to recalculate or not according to the extraction precision. The results indicate that the mean detection rates of the advanced variogram method on experimental areas 1 and 2 are 91.58% and 9011%

respectively

whereas the mean false alarm rates are 1983% and 3187%

respectively. Compared with the traditional and minimum distance methods

the proposed method not only maintains a relatively high detection rate but also decreases the false alarm rate significantly. The precision of rural built-up area extraction basically satisfies the requirements of practical application. However

the method still has several drawbacks. First

the "edge effect"

which refers to the phenomenon that the edge of built-up areas extracted from the image are actually wider than the actual edge

accompany almost every part of the extracted built-up areas. Meanwhile

this methodology is unable to recognize regions sharing similar texture features with built-up areas

such as the highlighted thready road and lumpish arable land

leading to false recognition of built-up areas. To solve the aforementioned problems

a method to restrain the high value of the variogram in places adjacent to the boundary of building areas and preprocessing are needed to ensure that certain texture features symbolize only the built-up areas.