It is of great significance to study the method of extracting urban features from GF-2 remote sensing data.Taking the urban area of Jixi City as the study area,and the GF-2 image is used as the data source.The image is divided into multiple scales,the classification rules of the corresponding objects are established,and the object-based classification method of the rule set is used to classify the objects.Compare with SVM supervised classification results.The results show that the overall accuracy of object-oriented classification is 92.52%,and the Kappa coefficient is 0.91,which is significantly higher than the SVM supervised classification.Using the object-oriented classification method to classify the GF-2 image is better and the precision is higher.Object-oriented classification method based on GF-2 data is an effective method for extracting urban land use classification.
For the traditional remote sensing image registration method,there are too many pairs of registration error matching,and the efficiency of registration is low.In order to further improve the accuracy and efficiency of remote sensing image registration,a remote sensing image registration method based on wavelet was proposed.Firstly,the feature extraction of the reference image and the image to be registered using the Marr wavelet in scale space theory.Then use Euclidean distance to perform initial registration of the feature points of the reference image and the image to be registered.Again consistent with the random sampling method,the registration results for early registration for fine.Experimental results show that this method can effectively eliminate false matching points compared with SIFT and other improved SIFT algorithms.Improve registration accuracy,while improving the efficiency of more than double registration.Conclusion:for traditional remote sensing image registration methods,registration mismatches have many pairs of points and the efficiency is low.This paper presents an accurate remote sensing image registration method.The experimental results show that this method can effectively improve the accuracy of registration and reduce the time of registration.
There are numerous islands with abundant resources in China.Due to the limited information included in common polarization features and the poor effect of traditional classification methods when there are few samples,nine polarization features are analyzed and classification is carried out using active deep learning.Firstly,multiple features are extracted from an original image.Then,the original features can be extracted by anto\|encoder and the initial classifier is trained and fine-tune the whole model with a small number of labeled samples.Finally,the most uncertain samples are selected to label with active learning algorithm and added to the training samples.Experiment comfirms that active deep learning can effectively improve the classification accuracy with less labeled samples and entropy shannon is a more effective feature to distinguish between seawater,mudflats and beaches.