A Discriminative Feature Learning Approach With Distinguishable Distance Metrics for Remote Sensing Image Classification and Retrieval

The fast data acquisition rate due to the shorter revisit periods and wider observation coverage of satellites results in large amounts of remote sensing images every day. This brings the challenge of how to accurately search the images with similar visual content as the query image. Content-based image retrieval (CBIR) is a solution to this challenge, its performance heavily depends on the effectiveness of the image representation features and similarity evaluation metrics. Ideal image feature representations have dispersed interclass, compact intraclass distribution. However, the neural networks employed by many CBIR methods are trained with cross entropy loss, which does not directly optimize the metrics that evaluates interclass variance over intraclass variance, hence, their feature representations are suboptimal. Meanwhile, the traditional distance metrics used by many CBIR methods cannot index the similarity of feature representations well in high-dimensional space. For better CBIR performance, we propose a discriminative feature learning approach with distinguishable distance metrics for remote sensing image classification and retrieval. By balancing the diagonal elements and nondiagonal elements of the within-class scatter matrix of deep linear discriminant analysis, our proposed loss function, balanced deep linear discriminant analysis, can better optimize the Rayleigh–Ritz quotient, which measures interclass variance over intraclass variance. In addition, the proposed distance metrics, reciprocal exponential distance (RED), is more capable of maintaining distance contrast in high dimensionality, therefore, it can better index similarity for feature representations in high dimensionality. Both visual interpretations and quantitative metrics of extensive experiments demonstrated the effectiveness of our approach.