Research On Technologies Of High Resolution Optical Remote Sensing Image Retrieval

With the advent of the era of remote sensing big data,the rapid and high-precision to organization,management and retrieval of massive high-resolution remote sensing image data,whose goal is to meet users' quick browsing and efficient retrieval of interesting targets,has become an urgent problem in the acquisition of remote sensing information.Feature extraction and similarity matching are two important processes of remoting sensing image retrieval,which directly determine the accuracy and the speed of retrieval.To solve the above problems,the researches on feature extraction and similarity matching in high-resolution optical remote sensing image retrieval processes are studied.The main contents are as follows.(1)Aiming at the problem that the ImageNet pre-training model to extract features leads to poor retrieval performance due to some high-resolution optical remote sensing images have some characteristics,such as low illumination,complex content and rich details,a high-resolution feature extraction model combining with image enhancement and convolutional neural network for remote sensing images is proposed.Three image enhancement algorithms and three convolutional neural network structures were trained on the remote sensing image dataset through the pre-training model of ImageNet network.After training,nine types of remote sensing image features based on "image enhancement+convolutional neural network" were obtained.Through comparative analysis of retrieval experiments,a high-resolution remote sensing image feature extraction model with the better feature extraction effect is obtained,which effectively solves the problem of feature extraction of high-resolution remote sensing images using convolutional neural networks trained with ordinary image datasets.(2)Aiming at the problem that the feature dimension of the image is high after feature extraction,leading to the influences of the retrieval speed and accuracy,a feature vector optimization method combining PCA and t-SNE is proposed.The high-dimensional data after feature extraction is linearly reduced to 128 dimensions for the first time,which ensures the time for image features in high-dimensional space to be mapped to low-dimensional space,and the retrieval speed is improved.Then,the feature vector obtained is reduced to 64 dimensions by second-order nonlinear dimension reduction and its coordinates are used as the distribution of t in the low-dimensional data,so that the distance between the clusters is extended to obtain a better solution.This guarantees the accuracy of mapping the image features in the high-dimensional space to the low-dimensional space,and improves the retrieval accuracy.(3)Aiming at the problem that the retrieval precision of similarity matching using a single distance formula is not high,a multi-distance combined Top-k ranking method is proposed.The four types of distance vectors between the query image and other image feature vectors in the remote sensing image library are sorted from small to large by using Quicksort,and the first k of each distance are selected from the ranking results to form a similarity matrix.The weights are assigned to the first k elements of the four types of distances,and the weights of 1 to k are assigned to the first k elements of each distance from small to large.The weights of the same elements in the first k elements in the four types of distances are summed,and the results of the weighted sums are sorted in ascending order.The first k weights with small sum are selected as the result output,which improves the retrieval accuracy and speed.(4)Aiming at the time-consuming problem of image similarity distance comparison due to the similarity between similar types of images in high-resolution optical remote sensing images is low and the similarity between different types of images is high,a high-resolution remote sensing image retrieval method based on improved fuzzy C-means clustering is proposed.This method can calculate four kinds of distances after featuring extraction and optimization,and normalize processing these four distance feature vectors to obtain the similarity matrix,which is used as the input of FCM.Clustering image feature vectors reduces the input dimensions during clustering.The clustered results are matched by the multi-distance combined Top-k ranking method,which effectively reduces the time complexity in ranking and speeds up similarity matching.The experimental results on two high-resolution remote sensing image datasets show that this proposed method can effectively improve the retrieval accuracy and speed,and achieve good retrieval results and performance.(5)Aiming at the problem that training samples during SVM classification lead to time-consuming training of the optimal hyperplane,too many negative examples in the training sample affect the construction of the classification hyperplane,and the test of the SVM model yields the problem of incorrect classification results,a multi-range combined Top-k ranking method is proposed to filter the SVM training sample set.Before the SVM classification,the multi-distance combined Top-k ranking method is used to properly filter the training set and the filtered training set is used to train the optimal hyperplane of the SVM.The final search result is obtained according to the ranking results of the test sample data to the classified hyperplane distance.This method can reduce the number of samples in the training set.and at the same time,filter out most images that are not similar to the query images to avoid the influence of more dissimilar images on the classification results.The experimental results on two high-resolution remote sensing image datasets show that the proposed method can effectively improve the retrieval accuracy and speed,and has certain advantages in retrieval results and performance.(6)Aiming at the problem that the accuracy of the initial retrieval of some kinds of remote sensing images cannot meet the user's requirements,a method of relevance feedback based on distance evaluation criteria is proposed.The positive sample images in the previous return result are not labeled,and only the least similar images are labeled with negative examples,which reduces the number of labeling by the user.The labeling uses small sample labels to adjust the image that meets the distance evaluation criteria,avoiding the time-consuming phenomenon of multiple feedback retraining on the optimal hyperplane,and reducing the number of feedbacks through an iterative strategy.Experimental results show that this method can obtain ideal search results with fewer feedback times.