Exploring the relevance between images and their respective natural language descriptions, due to its paramount importance, is regarded as the next frontier in the general computer vision literature. Thus, recently several works have attempted to map visual attributes onto their corresponding textual tenor with certain success. However, this line of research has not been widespread in the remote sensing community. On this point, our contribution is three-pronged. First, we construct a new dataset for text-image matching tasks, termed TextRS, by collecting images from four well-known different scene datasets, namely AID, Merced, PatternNet, and NWPU datasets. Each image is annotated by five different sentences. All the five sentences were allocated by five people to evidence the diversity. Second, we put forth a novel Deep Bidirectional Triplet Network (DBTN) for text to image matching. Unlike traditional remote sensing image-to-image retrieval, our paradigm seeks to carry out the retrieval by matching text to image representations. To achieve that, we propose to learn a bidirectional triplet network, which is composed of Long Short Term Memory network (LSTM) and pre-trained Convolutional Neural Networks (CNNs) based on (EfficientNet-B2, ResNet-50, Inception-v3, and VGG16). Third, we top the proposed architecture with an average fusion strategy to fuse the features pertaining to the five image sentences, which enables learning of more robust embedding. The performances of the method expressed in terms Recall@K representing the presence of the relevant image among the top K retrieved images to the query text shows promising results as it yields 17.20%, 51.39%, and 73.02% for K = 1, 5, and 10, respectively.

Ongoing discoveries of water reserves have fostered an increasing adoption of crop circles in the desert in several countries. Automatically quantifying and surveying the layout of crop circles in remote areas can be of great use for stakeholders in managing the expansion of the farming land. This letter compares latest deep learning models for crop circle detection and counting, namely Detection Transformers, EfficientDet and YOLOv5 are evaluated. To this end, we build two datasets, via Google Earth Pro, corresponding to two large crop circle hot spots in Egypt and Saudi Arabia. The images were drawn at an altitude of 20 km above the targets. The models are assessed in within-domain and cross-domain scenarios, and yielded plausible detection potential and inference response.

Recently, vision-language models based on transformers are gaining popularity for joint modeling of visual and textual modalities. In particular, they show impressive results when transferred to several downstream tasks such as zero and few-shot classification. In this article, we propose a visual question answering (VQA) approach for remote sensing images based on these models. The VQA task attempts to provide answers to image-related questions. In contrast, VQA has gained popularity in computer vision, in remote sensing, it is not widespread. First, we use the contrastive language image pretraining (CLIP) network for embedding the image patches and question words into a sequence of visual and textual representations. Then, we learn attention mechanisms to capture the intradependencies and interdependencies within and between these representations. Afterward, we generate the final answer by averaging the predictions of two classifiers mounted on the top of the resulting contextual representations. In the experiments, we study the performance of the proposed approach on two datasets acquired with Sentinel-2 and aerial sensors. In particular, we demonstrate that our approach can achieve better results with reduced training size compared with the recent state-of-the-art.

In this letter, we formulate a land-use (LU) classification problem within a compressive sensing (CS) fusion framework. CS aims at providing a compact representation form after a given query image has been processed with an opportune feature extraction type. In particular, residuals are generated from the image reconstruction with dictionaries associated with the available set of possible LUs and gathered to form a single-feature image pattern. The patterns obtained from different types of features are then fused to provide the final LU estimate. Two simple fusion strategies are adopted for such purpose. As demonstrated by experiments ran on the basis of a public benchmark database, the proposed method can achieve substantial classification accuracy gains over reference methods.