Semantic segmentation of remote sensing ship targets is one of the most challenging works in image processing, especially for small and multi‐scale ship target detection. To solve these problems, an efficient method based on convolutional neural networks (CNN) to detect ship targets is proposed. This method introduces the attention model to the network to enhance the characteristics of small targets and combines atrous convolution with traditional CNN to increase the receptive field. To preserve the information lost by pooling, the proposed method uses the passthrough layer method to retain more features and concatenate the high‐ and low‐resolution features. To verify the effectiveness of the method proposed in this study, the performance was evaluated by using precision, recall, F1‐Score, mean intersection‐over‐union (IoU), and pixel accuracy measurements. These performances are all higher than the traditional semantic segmentation network SegNet. Mean IoU increases to 0.783 and pixel accuracy increases to 0.935. This method can conclusively identify ship targets in remote sensing images and has a certain reference value for remote sensing target detection.

Noise equivalent temperature difference (NETD) is an important figure of merit for infrared detectors. Lower NETD means that the detector can realize smaller temperature resolution and longer range recognition. In this work, we investigated the device performance, especially the NETD characteristics of a long-wavelength infrared (LWIR) photodetector based on InAs/GaSb type II superlattice (T2SL). To achieve mK-level temperature-sensitivity, the effect of the detector intrinsic and extrinsic factors on NETD performance has been discussed. Through a series of efforts, a NETD value of 7mK was realized for a T2SLs detector with a 100% cutoff wavelength of 11.4 m at 77 K.

The inherent non-uniformity of Infrared Focal Plane Arrays (IRFPA) inevitably results in stripe noise, which severely degrades image quality and hinders downstream applications. Existing deep learning methods often struggle to strike a balance between effective denoising and the preservation of fine thermal textures. To address this issue, we propose a Bottleneck-free Channel Dual-path Aggregation Network (BCDA-Net) based on a “Perception-Reconstruction” design principle. In the perception stage, the network jointly employs the Dual-Path Channel Down-sampling (DCD) module and the Context-Guided Stripe Attention Block (CGSAB). The DCD module utilizes a channel split strategy to simultaneously extract semantic features and preserve high-frequency textures, while the CGSAB performs global context modeling on these features to precisely perceive and locate global stripe noise patterns. In the reconstruction stage, we integrate the Cascaded Dense Feature Aggregation (CDFA) module with a Bottleneck-Free Aggregation Strategy (BFAS). The CDFA utilizes the perceived information to densely aggregate features and progressively reconstruct clean image details, whereas the BFAS structurally blocks the propagation of low-resolution noise during decoding, effectively mitigating aliasing artifacts induced by deep feature upsampling. Together, these components form a complete closed loop from accurate noise perception to high-fidelity reconstruction. Extensive experiments on public and real-world datasets demonstrate that BCDA-Net maximally preserves image details while removing non-uniform stripe noise. Both objective metrics and subjective visual quality outperform existing state-of-the-art methods.

To ensure a high signal-to-noise ratio and high image volume, a geostationary orbiting ocean remote-sensing system needs to maintain high platform stability over a long integration time because it is affected by satellite attitude changes. When the observation target is the ocean, it is difficult to extract image features because of the lack of characteristic objects in the target area. In this paper, we attempt to avoid using image data for satellite attitude and image pixel offset estimation. We obtain the satellite attitude by using equipment such as gyroscopes and performing time registration between the satellite attitude and the image data to achieve pixel offset matching between images. According to the law of satellite attitude change, we designed a Kalman-like filter fitting (KLFF) algorithm based on the satellite attitude change model and the Nelder–Mead search principle. The discrete attitude data were time-marked by a synchronization system, and high-precision estimation of the satellite attitude was achieved after fitting with the KLFF algorithm. When the measurement accuracy of the equipment was 1.0 × 10⁻³°, the average prediction error of the algorithm was 1.09 × 10⁻³°, 21.58% better than the traditional interpolation prediction result of 1.39 × 10⁻³°. The peak value of the fitting angle error reached 2.5 × 10⁻³°. Compared with the interpolation prediction result of 6.2 × 10⁻³°, the estimated stability of the satellite attitude improved by about 59.68%. After using the linear interpolation method to compensate for the estimated pixel offset, its discrete range was 0.697 pixels. Compared with the 1.476 pixels of the interpolation algorithm, it was 52.8% lower, which improved the noise immunity of the algorithm. Finally, a KLFF algorithm was designed based on the satellite attitude change model by using external measurement data and the synchronous clock as a benchmark. The instantaneous attitude of the satellite was accurately estimated in real time, and the offset matching between the images was realized, which lays the foundation for in-orbit satellite data processing.