Web-based remote sensing image retrieval using multiscale and multidirectional analysis based on Contourlet and Haralick texture features

Abstract

PurposeExtracting suitable features to represent an image based on its content is a very tedious task. Especially in remote sensing we have high-resolution images with a variety of objects on the Earth's surface. Mahalanobis distance metric is used to measure the similarity between query and database images. The low distance obtained image is indexed at the top as high relevant information to the query.Design/methodology/approachThis paper aims to develop an automatic feature extraction system for remote sensing image data. Haralick texture features based on Contourlet transform are fused with statistical features extracted from the QuadTree (QT) decomposition are developed as feature set to represent the input data. The extracted features will retrieve similar images from the large image datasets using an image-based query through the web-based user interface.FindingsThe developed retrieval system performance has been analyzed using precision and recall and F1 score. The proposed feature vector gives better performance with 0.69 precision for the top 50 relevant retrieved results over other existing multiscale-based feature extraction methods.Originality/valueThe main contribution of this paper is developing a texture feature vector in a multiscale domain by combining the Haralick texture properties in the Contourlet domain and Statistical features using QT decomposition. The features required to represent the image is 207 which is very less dimension compare to other texture methods. The performance shows superior than the other state of art methods.

Steel surface defect classification approach using an All-optical Neuron-based SNN with attention mechanism

“…Finally, they employed a support vector machine (SVM) to accurately classify defects in steel strip surface images (Hu et al, 2014). In addition, researchers have employed image processing techniques such as curvelet transform (Krishnan et al, 2021), wavelet transform (Sun and Che, 2017) and others to treat faulty images. Ashour et al presented a novel approach to identify steel surface defects.…”