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Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future. [ABSTRACT FROM AUTHOR]

Automatic extraction of building contours from high-resolution images is of great significance in the fields of urban planning, demographics, and disaster assessment. Network models based on convolutional neural network (CNN) and transformer technology have been widely used for semantic segmentation of buildings from high resolution remote sensing images (HRSI). However, the fixed geometric structure and the local receptive field of the convolutional kernel are not good at global feature extraction, and the transformer technique with self-attention mechanism introduces computational redundancies and extracts local feature details poorly in the process of modeling the global contextual information. In this paper, a dual-branch fused reconstructive transformer network, DFRTNet, is proposed for efficient and accurate building extraction. In the encoder, the traditional transformer is reconfigured by designing the local and global feature extraction module (LGFE); the branch of global feature extraction (GFE) performs dynamic range attention (DRA) based on the idea of top-k attention for extracting global features; furthermore, the branch of local feature extraction (LFE) is used to obtain fine-grained features. The multilayer perceptron (MLP) is employed to efficiently fuse the local and global features. In the decoder, a simple channel attention module (CAM) is used in the up-sampling part to enhance channel dimension features. Our network achieved the best segmentation accuracy on both the WHU and Massachusetts building datasets when compared to other mainstream and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In recent years, researchers have made significant strides in computer vision by leveraging transformers, achieving remarkable breakthroughs in low-level vision tasks. The inherent long-range dependency of transformers grants them potent remote modeling capabilities, surpassing those of Convolutional Neural Networks (CNNs) and enabling the extraction of global features and accurate semantic structures. However, it has been observed that single transformer frameworks lack sensitivity to high-frequency information in images, resulting in the generation of blurry reconstructed regions. To address this limitation, this paper proposes a novel two-branch Dual Frequency Feature Fusion Network (DF3Net) for image inpainting based on the hierarchical atrous transformer (HAT). Specifically, the head of the dual-frequency convolution (DFC) module decouples the feature maps into low and high-frequency components. The low-frequency factor goes through the proposed HAT branch, while the high-frequency component is input into the gated convolution module branch, effectively capturing both global structural information and local texture details. Finally, the DFC tail fuses the high and low-frequency features to output the reconstructed image. Moreover, the feature fusion between high and low-frequency branches is performed layer-wise within the network, enabling mutual learning between the two branches interactively, ensuring coherence in image semantic structure and texture details. Experimental evaluations on Places2, Paris StreetView, and CelebA-HQ with different mask ratios demonstrate that the proposed method outperforms state-of-the-art methods in enhancing the structural accuracy of image inpainting. It generates semantically reasonable images with fine texture details. • It proposes a double-branch dual frequency feature fusion network combining transformer and CNNs. • It introduces a hierarchical atrous transformer structure for low-frequency feature extraction. • It develops a dual frequency convolution module by applying the Fourier transformation to the feature maps. • It could achieve competitive results on three popular datasets of different sceneries. [ABSTRACT FROM AUTHOR]