# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues  
[PERSON]  
1 Applied Computer Science Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia; [EMAIL_ADDRESS]  
2 Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; [EMAIL_ADDRESS]  
3 Department of Studies in Computer Science, University of Mysore, Mysore 570006, India; [EMAIL_ADDRESS]  
4 Department of Information Engineering and Computer Science, University of Trento, 28123 Trento, Italy; [EMAIL_ADDRESS]  
5 Correspondence: [EMAIL_ADDRESS]; Tel. : +96-610-1469-6297  
20 November 202  
the spatial localization) of certain objects across a given image volume. Image classification schemes typically attempt to tie meaningful feature representations (i.e., either spatial and/or spectral) to an efficient classification model, which has been shown to produce plausible results. On the other hand, image retrieval retrieves a query image (or an ensemble thereof) from a large-scale archive of image data. Efficient image retrieval methods do not only aim to achieve high retrieval scores but also to cope with the processing overheads due to the size of the retrieval archive. Furthermore, the advent of powerful processing machines has paved the way for deep architectures that have significantly improved the performance of remote sensing image classification and retrieval [6; 7; 8; 9; 10; 11; 12]. Traditional image classification schemes, owing to their discrete nature (i.e., single labeling), remain inefficient with respect to the abundant visual information an image may uncover (i.e., where visual content outweighs discrete semantic attributes). As opposed to traditional single-label image classification, image multi-labeling balances the visual attributes of images with respect to their semantic tenor, which is commonly termed the semantic gap. To address this problem, the computer vision community has focused on developing multi-label approaches to reduce the semantic gap and improve the retrieval efficiency. For example, in [13], a multi-label technique for detecting an indoor scene was proposed to help blind people sense nearby objects around them. This technique gave rise to similar approaches in remote sensing [14; 15]. Inspired by this, another work [16] proposed a multi-label for remote sensing image retrieval archive, where one or more labels based on visual inspection have been assigned for each image in the archive is manually. However, coherence between the labels themselves remains questionable because multiple labels are assigned to an image without taking in conseccration their semantic reciprocity. Therefore, incorporating this element (i.e., relativity among the objects) can elevate the quality of semantic descriptions. It is evident that image matching has been a major interest [17; 18; 19]. However, there have been limited works on text image matching in the field of remote sensing, possibly because of the complexity of remote sensing images. One of the few examples include the work in [18], which is similar to that in [20], that uses convolutional neural networks (CNNs) to extract image descriptors and a sequence long short term memory (LSTM) model to generate a sentence describing the remote sensing images. Recent works from the general literature have suggested that by addressing the retrieval problem from a cross-modal text-image, instead of image-to-image, the learning perspective seems to offer a more practical solution. This concept has attracted much attention in recent studies, particularly in image captioning, where images are combined with natural language processing. Essentially, it generates a sequential text that describes visual data, which is similar to how humans realize it.