We present algorithms for the automatic detection of oil spills in SAR images. The developed framework consists of first detecting dark spots in the image, then computing a set of features for each dark spot, before the spot is classified as either an oil slick or a "lookalike" (other oceanographic phenomena which resemble oil slicks). The classification rule is constructed by combining statistical modeling with a rule-based approach. Prior knowledge about the higher probability for the presence of oil slicks around ships and oil platforms is incorporated into the model. In addition, knowledge about the external conditions like wind level and slick surroundings are taken into account. The presented algorithms are tested on 84 SAR images. The algorithm can discriminate between oil slicks and lookalikes with high accuracy. 94% of the oil slicks and 99% of the lookalikes were correctly classified.
Archaeological sites are sometimesvisible in satelliteimages as soilorcrop marks.At best, the marks are distinct, but they tend to have less contrast with the background than many other patterns in the images. Consequently, reliable automated detection based on pattern recognition is very difficult. Our method detects circle-shaped soil and crop marks in the panchromatic band of high-resolution satellite images of agricultural fields. Such circular marks may be caused by burial mounds. In our approach, local contrast enhancement is applied in order to make weak marks more distinct.The image is then convolved with ring templates of varying sizes, giving high absolute values at candidate circular mark locations.Each candidate mark is presented to an operator, who may reject it.We tested our method on Quickbird images from southeast Norway. The number of detected candidate marks could be varied by changing a threshold value. A reasonable compromise between not detecting too many false rings and at the same time detecting as many true rings as possible, might be when the numberoffalse detectionsisapproximately seventimesthe numberoftrue detections.Inthis case, 11out of15, or 73%, of the strong rings were detected, and 5 out of10, or 50%, of the fairly strong rings were detected.This is 16 out of 25 of the strong and fairly strong rings, or 64%. Archaeologists state that the software tool we developed will be helpful for locating potential cultural heritage sites. Although it makes many false detections, it will relieve the operators from time-consuming manual inspection of entire images.
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