Remote Sensing Image Retrieval by Deep Attention Hashing With Distance-Adaptive Ranking

Abstract

With the joint advancement of numerous related fields of remote sensing, the amount of remote sensing data is growing exponentially. As an essential remote sensing Big Data management technique, content-based remote sensing image retrieval has attracted more and more attention. A novel deep attention hashing with distance-adaptive ranking (DAH) is proposed for remote sensing image retrieval in this article. First, a channel-spatial joint attention mechanism is employed for feature extraction of remote sensing images to make the proposed DAH method focus more on the critical details of the remote sensing images and suppress irrelevant regional responses. Second, a novel balanced pairwise weighted loss function is proposed to enable discrete hash codes to participate in neural network training, which contains pairwise weighted similarity loss, classification loss, and quantization loss. The pairwise weighted similarity loss is designed to decrease the impact of the imbalance of positive and negative sample pairs. The classification loss and quantization loss are added to the loss function to decrease background interference and information loss during the quantization phase, respectively. Finally, a distanceadaptive ranking strategy with category-weighted Hamming distance is presented in the retrieval phase to utilize the category probability information fully. Experiments on benchmark datasets compared with state-of-the-art methods demonstrate the effectiveness of the proposed DAH method.

Attention-based localized hash retrieval for large-scale remote sensing images using deep feature splitting strategy

“…The PatternNet dataset shows a slight increase in performance than the proposed model in DAH. 33 However, by employing the DFS-LHash strategy, we achieve better results than some deep hashing approaches listed in Table 5.…”

“…32 A deep attention hashing with distance-adaptive ranking is presented. 33 A fine-aligned discriminative hashing is designed for cross-modal remote sensing image-audio retrieval. 34 An adversarial cascaded hashing for cross-modal vessel image retrieval is proposed.…”

An Image Retrieval Method for Lunar Complex Craters Integrating Visual and Depth Features

“…This approach automatically extracts image features through deep learning models and optimizes the distances between features through metric learning, making the distances in feature space closer for similar images while expanding the distances between dissimilar images. Deep metric learning has shown significant effectiveness in multiple applications in the field of remote sensing, including image retrieval [21][22][23][24][25], image classification [26,27], and object recognition [28,29], etc. However, the task of image retrieval for lunar impact craters demands more complex and meticulous feature extraction requirements, and these models do not always effectively capture all the key features of the craters.…”