Few-shot learning has achieved great success in computer vision. However, when applied to Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR), it tends to demonstrate a bad performance due to the ignorance of the differences between SAR images and optical ones. What is more, the same transformation on both images may cause different results, even some unexpected noise. In this paper, we propose an improved Prototypical Network (PN) based on Spatial Transformation, also known as ST-PN. Cascaded after the last convolutional layer, a spatial transformer module implements a feature-wise alignment rather than a pixel-wise one, so more semantic information can be exploited. In addition, there is always a huge divergence even for the same target when it comes to pixel-wise alignment. Moreover, it reduces computational cost with fewer parameters of the deeper layer. Here, a rotation transformation is used to reduce the discrepancies caused by different observation angles of the same class. Thefinal comparison of four extra losses indicates that a single cross-entropy loss is good enough to calculate the loss of distances. Our work achieves state-of-the-art performance on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset.

Remote sensing images play a crucial role in remote sensing target detection and 3D remote sensing modeling, and the enhancement of resolution holds significant application implications. The task of remote sensing target detection requires a substantial amount of high-resolution remote sensing images, while 3D reconstruction tasks generate denser models from diverse view perspectives. However, high-resolution remote sensing images are often limited due to their high acquisition costs, a scarcity of acquisition views, and restricted view perspective variations, which pose challenges for remote sensing tasks. In this paper, we propose an advanced method for a high-resolution remote sensing novel view synthesis by integrating attention mechanisms with neural radiance fields to address the scarcity of high-resolution remote sensing images. To enhance the relationships between sampled points and rays and to improve the 3D implicit model representation capability of the network, we introduce a point attention module and batch attention module into the proposed framework. Additionally, a frequency-weighted position encoding strategy is proposed to determine the significance of each frequency for position encoding. The proposed method is evaluated on the LEVIR-NVS dataset and demonstrates superior performance in quality assessment metrics and visual effects compared to baseline NeRF (Neural Radiance Fields) and ImMPI (Implicit Multi-plane Images). Overall, this work presents a promising approach for a remote sensing novel view synthesis by leveraging attention mechanisms and frequency-weighted position encoding.

Monocular depth estimation using a single remote sensing image has emerged as a focal point in both remote sensing and computer vision research, proving crucial in tasks such as 3D reconstruction and target instance segmentation. Monocular depth estimation does not require multiple views as references, leading to significant improvements in both time and efficiency. Due to the complexity, occlusion, and uneven depth distribution of remote sensing images, there are currently few monocular depth estimation methods for remote sensing images. This paper proposes an approach to remote sensing monocular depth estimation that integrates an attention mechanism while considering global and local feature information. Leveraging a single remote sensing image as input, the method outputs end-to-end depth estimation for the corresponding area. In the encoder, the proposed method employs a dense neural network (DenseNet) feature extraction module with efficient channel attention (ECA), enhancing the capture of local information and details in remote sensing images. In the decoder stage, this paper proposes a dense atrous spatial pyramid pooling (DenseASPP) module with channel and spatial attention modules, effectively mitigating information loss and strengthening the relationship between the target’s position and the background in the image. Additionally, weighted global guidance plane modules are introduced to fuse comprehensive features from different scales and receptive fields, finally predicting monocular depth for remote sensing images. Extensive experiments on the publicly available WHU-OMVS dataset demonstrate that our method yields better depth results in both qualitative and quantitative metrics.

With the development of miniaturization technology, Synthetic Aperture Radar (SAR) can be equipped on small carriers such as small Unmanned Aerial Vehicles (UAVs). In order to lower the cost, the accuracy of navigation equipment carried by UAV SAR is usually limited, so it is challenging to meet the requirements of SAR imaging and locating accuracy. Therefore, accurately estimating SAR tracks becomes a crucial issue. So, for the motion error estimation model widely used in current literature, this paper derives the accuracy limits of the model for the first time. The derived Cramer–Rao Lower Bound (CRLB) specifies the factors affecting the estimation accuracy, which provides new insights into the estimation model. The in-depth analysis of how the factors affect CRLB can guide the setting of the parameters while using the estimation method. Moreover, based on the accuracy analysis model, this paper improves the WTLS-based autofocus method (WTA) by selecting the appropriate estimation kernel step. The proposed method can suppress noise more effectively and further ensure estimation accuracy compared to WTA. Airborne SAR data experiments in the high-resolution condition obtain trajectory estimation results of 0.02 m.