The development of the remotely sensed techniques enlarges the applications of the remote sensing imagery. The clustering of high resolution imagery is difficult, due to the fact that the minor objects, such as roads, make the appearance of the same category region non-uniform. This paper proposes a new approach to cluster high resolution remote sensing imagery. The new clustering approach includes three steps as the following: Firstly, eliminate the minor components in the moving windows. Secondly, compute the image features, such as the energy, some high order cumulants and central moments of pixels' values in moving windows. Lastly, apply the BPC neural network, which is combined by a Back-Propagation (BP) neural network and a Competive neural network, to cluster images according to the image features. Two methods, minimum distance method and the K-means method, are compared with the new clustering approach, proposed by this paper, by using SPOT images for clustering residential areas and agricultural areas in the suburbs of Beijing. The experimental results show that the new clustering approach has the higher clustering accuracy.
KEYWORDS: Edge detection, Signal to noise ratio, Digital filtering, Signal detection, Image processing, Interference (communication), Sensors, Digital imaging, Electronics, Pattern recognition
Good detection, good localization and single response to a single edge are the three performance criteria for edge detection. Based on the mathematical formulations about detection, localization and single response criterion, the paper explains the theory of how to attain the optimal edge detection operator in details. The procedure of designing edge detection operators for any shape edges is introduced by illustrating how to attain the one dimensional step edge optimal detection operator. Based on the three performance criteria, a new edge detector is proposed, which is named as sine-operator. The sine-operator outperforms the first derivative of Gauss function according to the above three criteria. The results show that the sine-operator proposed by the paper can obtain good performance for both one dimensional signal and two dimensional image with noise.
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