K-means Pelican Optimization Algorithm based Search Space Reduction for Remote Sensing Image Retrieval

In remote sensing field, the image retrieval is considered a complex task and attained higher attention, because of the data acquired from the earth observation satellites. An understanding of remote sensing images is obstructed because of the large amount of remote sensing images, lack of labeled samples, and complex contents. Content-based image retrieval made the powerful tool to mine huge remote sensing image databases. In content-based image retrieval, the query image is given for acquiring the images with identical visual content from the huge amount of remote sensing image database. In this research, the K-means pelican optimization algorithm is proposed for ensuring the search space reduction to enhance the retrieval of remote sensing images. The different feature extraction approaches such as Resnet-18, gray level co-occurrence matrix, Color moments, and local binary pattern are used to perform an effective feature extraction. Further, the feature transformation and neighborhood component analysis based feature selection is performed to transform the features into the similar significance and to select optimum features. Three different datasets such as Aerial Image Dataset, Remote Sensing-Image Classification Benchmark-256 and Wuhan University-Remote Sensing datasets are used to evaluate the proposed K-means pelican optimization algorithm. The proposed method is analyzed using precision, recall, F1-score and Average Normalized Modified Retrieval Rank. The existing research such as gabor-channel attention-ResNet, squeeze and excitation networks with ResNet50 and fused convolutional neural network-relevance feedback model are used to compare the K-means pelican optimization algorithm. The precision of the K-means pelican optimization algorithm for the Aerial Image Dataset dataset is 96.29% which is high when compared to the gabor-channel attention-ResNet, squeeze and excitation networks-ResNet50 and fused convolutional neural network- relevance feedback model.