# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
For example, in [17], a semantic attention model was proposed to enhance image captioning via convolutional neural network (CNN) and recurrent neural network (RNNs) models to obtain high-level attributes from images, which has improved the description generation of the image and has produced interesting experimental results. Similarly, CNNs and RNNs were combined for image captioning in [20]. On the other hand, a different graph-based pipeline was proposed in [21]. Other studies have mainly tackled the problem of learning common representations that enable the textual description of visual data and vice versa [22; 23; 24; 25]. [PERSON] et al. [22] proposed a deep visual semantic embedding framework to use semantic information and labeled images to recognize visual objects. [PERSON] and [PERSON] [23] developed a deep model by using inferred latent alignment between the region of the image and the segment of the text that describes it to generate a description of image regions. [PERSON] et al. [24] developed an approach for image and text embedding by using a dual-branch deep network with multiple layers. [PERSON] et al. [25] implemented a two-branch deep network for text-image embedding as well as a similarity network to determine the correspondence of an image-text pair. [PERSON] et al. [26] proposed a hashing technique to build a discrete supervised hashing model for cross-modal retrieval to learn a potent similarity matrix. [PERSON] et al. [27] proposed a framework that utilizes LSTM to represent the sentence, CNN to learn the representation of the image, and triplet loss to push the matched image-sentence pair closer to each other than the unmatched pairs in the embedding space. [PERSON] et al. [28] introduced a multi-regional multi-label CNN to learn image semantic concepts and LSTM for representing the sentence. [PERSON] et al. [29] adopted a tree-structured LSTM to learn the hierarchical relations between images and sentences, in addition to learning the relation between phrases and visual objects. [PERSON] et al. [30] developed a cross-modal projection matching/classification using a CNN to encode visual image features and LSTM to extract text features. It appears clearly from the literature of computer vision that the core problem in image retrieval is given a query one is interested to retrieve the most similar image in the database. This query can be an image or textual descriptions or a combination of them. Compared to image-image retrieval, text-image retrieval has been less investigated in the remote sensing community, possibly due to the complexity of appropriately tying textual data to respective visual representations. To cope with these limitations, the authors in [31] have proposed a new dataset for text-to-image matching named TextRS. They used a Deep Bidirectional Triplet Network (DBTN) for matching text to images based on CNN and LSTM networks. In this work, we propose an alternative approach based on asymmetric Siamese network. The first branch of this network uses BiT models for image representation, while the second branch relies on bidirectional Long Short-Term Memory (Bi-LSTM) for text encoding. The image and text representations are normalized and projected in a low dimensional space. The embedding features of the pair image-text should be invariant, while the features of different images and text instances should spread-out. The experimental results obtained on TextRS and Merced datasets are reported and discussed. The paper is structured as follows. In Section 2, we introduce the proposed methodology. While, in Section 3, we present the experimental results. Finally, conclusion and future developments for the proposed work are declared in Section 4.