The present invention discloses an SAR image circular oil depot detection method based on multi-dimensional salient feature clustering, and belongs to the technical field of remote sensing image processing and image recognition. The implementation process comprises: 1) introducing a mean filter to perform low-pass filtering with edge preservation characteristics on an entire SAR image; 2) extracting luminance features, texture features and structural features in the SAR image, and generating multi-dimensional salient feature vectors of the SAR image; 3) using a k-means clustering algorithm tocluster multi-dimensional salient feature vectors, calculating a saliency value of the cluster, and generating an SAR image saliency map; 4) according to the generated saliency map, using an active contour model to obtain top information of an oil depot; and 5) combining the oil depot diameter information to accurately obtain bottom information of the oil depot. Compared with the traditional method, by using the method provided by the present invention, accurate detection of the SAR image circular oil depot is realized, and the method provided by the present invention has important significance for port construction, resource investigation and petroleum resource reserve analysis.

SAR image circular oil depot detection method based on multi-dimensional salient feature clustering

The invention provides a SAR image target recognition method based on multi-feature fusion The method comprises the following steps: carrying out edge extraction on an SAR image, and then obtaining the compactness, the fullness and the complexity of the target; determining the gray level co-occurrence matrix of the SAR image, and utilizing the gray level co-occurrence matrix to calculate the texture feature quantity; constructing a multi-layer superpixel set for the SAR image, and constructing a non-equilibrium binary graph based on the multi-layer superpixel set, and separating the image target from the background through clustering; calculating a covariance matrix by means of a fused image feature matrix, and constructing an optimal projection matrix, and projecting the training sampleto the optimal projection matrix to obtain a sample after dimensionality reduction; in each round of training, counting the weak classification by means of the weight values of the eigenvalues of different training data samples, selecting a weak classifier according to different classification error rates of each eigenvalue, and weighting and summing the weak classifiers to construct an output classifier According to the technical scheme, the accuracy of image target recognition can be improved

SAR image target recognition method based on multi-feature fusion

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SAR image target recognition method based on multi-feature fusion