Agricultural land-use statistics are more informative per-field than per-pixel. Land-use classification requires up-to-date field boundary maps potentially covering large areas containing thousands of farms. This kind of map is usually difficult to obtain. We have developed a new, automated method for deriving closed polygons around fields from time-series satellite imagery. We have been using this method operationally in New Zealand to map whole districts using imagery from several satellite sensors, with little need to vary parameters. Our method looks for boundarieseither step edges or linear features-surrounding regions of low variability throughout the time series. Local standard deviations from all image dates are combined, and the result is convolved with a series of extended directional edge filters. We propose that edge linearity over a long distance is a more important criterion than spectral difference for separating fields, so edge responses are thresholded primarily by length rather than strength. The resulting raster edge map (combined from all directions) is converted to vector (GIS) format and the final polygon topology is built. The method successfully segments parcels containing different crops and pasture, as well as those separated by boundaries such as roads and hedgerows. Here we describe the technique and demonstrate it for an agricultural study site (4000 km 2) using SPOT satellite imagery. We show that our result compares favorably with that from existing segmentation methods in terms of both quantitative quality metrics and suitability for land-use classification.
The joint density for a particular trivariate chi-squared distribution given by the diagonal elements of a complex Wishart matrix is derived. This distribution has applications in the processing of multilook synthetic aperture radar data. The expression for the density is in the form of an infinite series that converges rapidly and is simple and fast to compute. The expression is shown to reduce to known forms for a number of special cases and is validated by simulation. The characteristic function is also derived and used to relate joint moments of the trivariate distribution to the parameters of the density function.
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