Edge detection of high-resolution imagery by integrating spectral and scale characteristics

The highly detailed information of objects can be provided in multi-scale by high-resolution remotely sensed imagery. When edge feature are detected in high-resolution image effectively, the internal geometric details also come to light but as noise form. In order to detect multi-scale edge feature and suppress noise, a novel method to detect the edge feature integrated spectral difference with wavelet transform was developed. Firstly, based on the theory of spectral angle, spectral difference normalized model (NSD) was defined to picture the contour of the object. Secondly, the dyadic wavelet transform was applied for each band to produce the multi-scale edge detail coefficients which actually are the gradient, and then weight the gradient magnitude of each band by using the cosine of gradient direction to enlarge the edge feature in the main gradient direction. Thirdly, combined with NSD, first fundamental form was used for detecting the gradient magnitude and orientation of multispectral images at different levels. Experiment by using QuickBird multispectral images are presented to demonstrated the representation efficiently. Compared with the results from wavelet transform and traditional edge detection operator, the proposed method can guarantee the edge without distortion, depict edge points more accurately and suppress more noise.