Abstract
A shape descriptor is an effective tool for describing the shape feature of an object in remote sensing images. Researchers have put forward a lot of excellent descriptors. The discriminability of some descriptors is very strong in the experiments, but usually their computational cost is large, which makes them unsuitable to be used in practical applications. This paper proposes a new descriptor-FMSCCD (Fourier descriptor based on multiscale centroid contour distance)—which is a frequency domain descriptor based on the CCD (centroid contour distance) method, multiscale description, and Fourier transform. The principle of FMSCCD is simple, and the computational cost is very low. What is commendable is that its discriminability is still strong, and its compatibility with other features is also great. Experiments on three databases demonstrate its strong discriminability and operational efficiency.
Highlights
In order to evaluate the performance of FMSCCD, CCD [18], FD-CCD [11], DIR [17], ASD&CCD [18], FPD [24], and MDM [16] were used for comparison
In the experiments, when FMSCCD combined with other descriptors, the weighted distance was used to calculate the dissimilarity between two shapes with Equation (16)
Each shape in the database is set as query in turn, In order to evaluate the performance of FMSCCD—CCD [8], FD-CCD [4], DIR [9], ASD&CCD
Summary
The sensing images are more blurred compared to in common images, The objects objectsininremote remote sensing images are more blurred compared to in common images, it is hard for with texture and pointand features, Figureas 1 shows. Without texture and feature points, people can only use shape features to identify objects. Shape descriptor is a features and feature points, people can only use shape features to identify objects. A shape descriptor great tool tool for the of identifying objects relying on shape features. Deep is a great fortask the task of identifying objects relying on shape features. For object recognition in dataset a verytask difficult such images not easy to obtain Theoretically, learning can complete object object recognition in remote sensingsensing imagesimages but establishing a training dataset deep learning can complete recognition in remote but establishing a training is a veryisdifficult since task such since images are not easyare to obtain.
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