17 November 1995 Segmentation of multitemporal ERS-1 SAR imagery
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Abstract
We consider SAR segmentation as an important step for the operational use of satellite SAR imagery in routine mapping exercises. The use of multi-temporal SAR imagery in this respect is of specific interest in areas where optical data are difficult to obtain, due to prevailing weather conditions. For areas where timely optical data are available, a hybrid approach can be adopted, still using the same segmentation algorithm described in this paper. In this paper we present the results of the application of a generic segmentation method on multi-temporal ERS-1 SAR imagery of the Dutch Flevoland agricultural area. The data were recorded during the fall of 1991, and constitute a series of 7 co-registered PRI images. Before segmentation, the data are filtered, using a maximum a priori filter technique and then byte-scaled to allow segmentation of any combination of (temporal) channels. We will evaluate the various channel combinations with respect to segmentation efficiencies. The results are compared to an existing data base of fixed field boundaries and a vector map of 1991 field boundaries derived from optical data sets (SPOT). Later we will compare the quality of field averaged PRI data extracted with polygons generated in the segmentation procedure with that from manually digitized field boundaries. One of our final objectives is to automatically generate multi-temporal backscattering signatures for the training of both supervised classification by means of neural networks [9] and supervised tillage monitoring [5]. Especially the potential to significantly advance the time of earliest estimates of crop acreage, by combining results from segmentation and knowledge based classification, is of interest in this framework.
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Ronald P.H.M. Shoenmakers, Guido G. Lemoine, Edmond Nezry, "Segmentation of multitemporal ERS-1 SAR imagery", Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226848; https://doi.org/10.1117/12.226848
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KEYWORDS
Image segmentation

Image filtering

Speckle

Synthetic aperture radar

Digital filtering

Edge detection

Image classification

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