With the growth of remote sensing images, un-derstanding image content automatically has attracted many researchers' interests in deep learning for remote sensing image. Inspired from the natural image captioning, the model with CNN-RNN as the backbone and supplemented by attention has been widely used in remote sensing image captioning. However, it is inefficient for the current attention layer to simultaneously mine hidden foreground from the background of remote sensing image and perform feature interactive learning. Meanwhile, the new mainstream language model has recently surpassed the traditional LSTM in sentence generation. For solving the above problems, in this paper, we proposed a novel thought to make the flat remote sensing images stereoscopic by separating the fore- and background. Based on hierarchical image informa-tion, we designed a novel Deformable Transformer equipped with deformable scaled dot-product attention to learn multi-scale feature from fore- and background through the powerful interactive learning ability. Evaluations are conducted on Four classic remote sensing image captioning datasets. Compared with the state-of-the-art methods, our Transformer variant achieves higher captioning accuracy.

With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditional signal deinterleaving algorithms rely heavily on manual experience threshold and have poor robustness. To address this problem, we designed an intelligent radar signal deinterleaving algorithm that was completed by encoding the frequency characteristic matrix and semantic segmentation network, named Sep-RefineNet. The frequency characteristic matrix can well construct the semantic features of different pulse streams of radar signals. The Sep-RefineNet semantic segmentation network can complete pixel-level segmentation of the frequency characteristic matrix and finally uses position decoding and verification to obtain the position in the original pulse stream to complete radar signals deinterleaving. The proposed method avoids the processing of threshold judgment and pulse sequence search in traditional methods. The results of the experiment show that this algorithm improves the deinterleaving accuracy and has a good against-noise ability of aliasing pulses and missing pulses.

In the field of object detection in large-scale remote sensing images, achieving a good tradeoff between model accuracy and speed has been a long-standing challenge. The majority of inference time is spent on background regions without objects, making real-time detection difficult in practical applications. Common approaches involve partitioning large-scale remote sensing images into smaller patches, followed by using additional classification networks or detectors on the final layer of the backbone's feature map to identify and filter out patches devoid of objects, ultimately enhancing detection efficiency. This paper proposes a novel model, called OPD-Swin-Transformer, for ship detection in large-scale remote sensing images. This model integrates a simple and lightweight Object Presence Detector (OPD) at each stage of the Swin-Transformer and uses a step-by-step, progressively challenging strategy to filter out background image patches, achieving an overall improvement in detection speed. The model optimizes the entire network end-to-end using a multi-task loss function, leading to simultaneous improvements in detection accuracy. By employing an optimal threshold generation strategy based on the weighted Youden index, the model effectively maintains a higher recall rate for ships while filtering out background images, achieving an optimal balance between speed and accuracy. Our OPD-Swin-Transformer is integrated into two mainstream detectors and evaluated on two popular benchmarks for ship detection. The experiments demonstrate that, when compared to other state-of-the-art methods, this approach increases inference speed by more than 40% while also improving detection accuracy. The code and models are publicly available at https://github.com/foreverfire01/OPD .

Fine-grained ship detection tasks require models to accurately classify fine-grained categories and precisely localize them within complex backgrounds, relying on detailed features. The challenges of this task mainly lie in bird’s-eye viewpoints, scale variations, rotational changes, and environmental factors, which lead to minor inter-class differences and significant intra-class variations. This paper presents a novel model, called Wavelet Transform-based Dual-Stream Backbone Network (WTDBNet), which effectively integrates three key strengths: the ability of the Transformer to model long-range dependencies for global context, the capability of convolutional neural networks to extract detailed local features, and the efficiency of wavelet transform in frequency-domain decomposition for enhancing edges and texture details. These components are fused via channel and spatial attention mechanisms, thereby improving the model’s ability to extract discriminative features. The effectiveness of WTDBNet is validated on two widely used benchmarks for fine-grained oriented ship detection, as well as on a self-constructed dataset designed to represent complex scenarios. Experimental results demonstrate the superior performance of the proposed method.

Knowledge distillation is an effective method for enhancing the performance of small neural networks. Existing distillation methods mainly involve extracting deep features from intermediate layers and using spatial matching techniques to enable the student model to learn from the teacher model’s knowledge. However, due to differences in the receptive field scales between large and small neural networks, the feature maps of the student and teacher models represent different regions of the image during the distillation process. Consequently, the student model cannot accurately acquire the knowledge of the teacher model, leading to performance degradation. To this end, we propose the Deformable Fast Transformer approach. Leveraging the inherent symmetry present in remote sensing images, such as airplanes and ships, our method adjusts the flipping angle of the student network’s feature map to mitigate the suboptimal distillation results caused by unequal receptive fields. As our method only involves calculations on the feature map, DFT can be applied to various models. We conducted experiments on models with different backbones, and the results demonstrate that the student network with our method achieves superior performance. For instance, in distillation experiments with ResNet110 as the teacher network and ResNet18 as the student network, the accuracy reached 72.01% on DOTA-v1.0, which are 1% higher than the baseline.