Remote sensing image retrieval (RSIR) frameworks encounter several issues in practical scenarios with (1) the dominating repetitive features in the final image representation, (2) inter- and intra‐class variability across objects, and (3) time complexity in exhaustively searching the large-scale remote sensing image archives. Motivated by these facts, we propose a deep feature-splitting approach that enhances a localized hashing (DFS-LHash) model for RSIR. The DFS strategy splits the fully connected (FC) layer features into equally sized blocks for a split-based localized hash learning, keeping VGG-16 as the baseline network. We incorporate effective deep clustering to improve classification performance by reducing the center-cluster loss. A block-wise softmax is applied on each block to pay more attention to the essential features with reduced dimension for object localization, which enhances retrieval performance. The center-cluster loss applied strengthens the class discriminant information and minimizes the distance between the feature descriptors belonging to similar classes to a greater extent. The classification error from the DFS strategy is reduced to learn highly discriminant and localized binary codes effectively. The proposed model provides effective search with better retrieval time and achieves state-of-the-art performance with mean average precision values of 97.42%, 96.06%, and 93.47% for the University of California- Merced, PatternNet and aerial image datasets, respectively.