Polarization imaging beyond traditional imaging offers a wide range of advantages, encompassing not only the detection of geometric shapes and surfaces, but also the measurement of physical properties. The divided focal plane (DoFP), an ideal real-time imaging, comprises a 2 × 2 linear polarization filter overlaid on a focal plane array sensor. Consequently, performing polarization demosaicing (PDM) becomes crucial to restore the missing components in the pixel data. The objective of PDM extends beyond the acquisition of intensity, and its primary goal is to minimize the estimation error of polarization characteristics. However, existing demosaicing networks often neglect the issues of polarization spatial-intensity attention and are trained similarly for generalization, thereby failing to effectively distinguish between genuine details and artifacts, which renders demosaicing vulnerable to distortion. In this work, we propose a novel generative adversarial network framework that integrates attention mechanisms and progressive discrimination for polarimetric images. The GAN-based framework introduces a global attention mechanism that enhances the interaction between spatial and polarization intensity information across different regions, resulting in improved network performance. Additionally, the framework generates artifact maps to normalize the model training process. To evaluate the effectiveness of our method, we assess its performance using full-resolution polarimetric image and compare it against traditional and recent PDM methods. The experimental results demonstrate that our proposed method achieves the highest peak signal-to-noise ratio (PSNR) and exhibits excellent visual quality for the output Stokes images when compared to existing methods.

Recently, Transformer-based methods have shown impressive performances in remote sensing image superresolution (RSISR). However, the application of Transformer in RSISR frequently results in artifacts and the loss of image detail due to limited information transmission pathways and the constraints of uni-dimensional self-attention mechanisms. To solve these problems, an Aggregation Connection Transformer (ACT-SR) is proposed for RSISR. ACT-SR employs an advanced attention mechanism designed to enrich information transmission across spatial and channel dimensions, thereby enlarging the receptive fields for more accurate feature extraction. A core component of ACT-SR is the novel aggregation connection attention block, which effectively captures spatial similarities and channel importance, aggregating this information through a combination of series and parallel connections for enhanced feature representation. Furthermore, a new gated feed-forward network is introduced to enhance the nonlinear mapping capabilities of the Transformer and control the information flow through the network. In addition, ACT-SR integrates a shifted windows scheme alongside interpolation residual calculation to facilitate efficient detail recovery and artifact elimination. Experimental results confirm the effectiveness of the proposed modules, with ACT-SR outperforming several state-of-the-art RSISR methods in both objective metrics and visual quality

The quadratic increase in computational complexity caused by global receptive fields has been a persistent challenge when applying Transformer-based methods in remote sensing image super-resolution (RSISR), involving high-resolution images. Channel attention (CA)-based Transformers offer an efficient approach with linear complexity by computing self-attention across the channel dimension. However, current CA-based Transformers suffer from performance degradation due to two main issues: constrained receptive field in the channel dimension caused by the multihead strategy and insufficient feature diversity resulting from the small size of attention matrices. To address these drawbacks, a novel shift channel attention Transformer (SCAT) is proposed for RSISR in this article. The core innovation of SCAT lies in its shift channel attention block (SCAB), which expands the receptive field by facilitating cross-head communication through a shift channel strategy. This design enables parallel computation of self-attention across multiple heads while ensuring robust cross-channel connections, thereby enhancing the global context modeling capabilities of the network. In addition, an attention supplementation module using depthwise convolution (DWC) is incorporated into SCAB to improve feature diversity. Finally, the proposed gated feedforward network utilizes the gating mechanism and DWC to effectively control the information flow and extract complementary spatial details. In the experiments, the effectiveness of these proposed modules is verified, and the SCAT model demonstrated superior performance in terms of quantitative and qualitative compared to several state-of-the-art RSISR methods.

Few-shot detection in remote sensing images has witnessed significant advancements recently. Despite these progresses, the capacity for continuous conceptual learning still poses a significant challenge to existing methodologies. In this paper, we explore the intricate task of incremental few-shot object detection (iFSOD) in remote sensing images. We present a pioneering transfer-learning-based technique, termed InfRS, designed to enable the incremental learning of novel classes using a restricted set of examples, while simultaneously preserving the knowledge learned from previously seen classes without the need to revisit old data. Specifically, we pretrain the detector using sufficient data from base datasets and then generate a set of class-wise prototypes that represent the intrinsic characteristics of the data. In the incremental learning session, we design a Hybrid Prototypical Contrastive (HPC) encoding module for learning discriminative representations. Furthermore, we develop a prototypical calibration strategy based on the Wasserstein distance to overcome the catastrophic forgetting problem. Comprehensive evaluations conducted with two aerial imagery datasets show that our InfRS effectively addresses the iFSOD issue in remote sensing imagery. Code is available at https://github.com/lyanna4869/InfRS.git.

Pansharpening refers to the fusion of a panchromatic (PAN) and a multispectral (MS) image aimed at generating a high-quality outcome over the same area. This particular image fusion problem has been widely studied, but until recently, it has been challenging to balance the spatial and spectral fidelity in fused images. The spectral distortion is widespread in the component substitution-based approaches due to the variation in the intensity distribution of spatial components. We lightened the idea using the total variation optimization to improve upon a novel GIHS-TV framework for pansharpening. The framework drew the high spatial fidelity from the GIHS scheme and implemented it with a simpler variational expression. An improved L1-TV constraint to the new spatial–spectral information was introduced to the GIHS-TV framework, along with its fast implementation. The objective function was solved by the Iteratively Reweighted Norm (IRN) method. The experimental results on the “PAirMax” dataset clearly indicated that GIHS-TV could effectively reduce the spectral distortion in the process of component substitution. Our method has achieved excellent results in visual effects and evaluation metrics.

Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications in quantitative and visualized analysis. However, a snapshot camera based on MSFA captures a single band per pixel; thus, the other spectral band components of pixels are all missed. The raw images, which are captured by snapshot multi-spectral imaging systems, require a reconstruction procedure called demosaicing to estimate a fully defined multi-spectral image (MSI). With increasing spectral bands, the challenge of demosaicing becomes more difficult. Furthermore, the existing demosaicing methods will produce adverse artifacts and aliasing because of the adverse effects of spatial interpolation and the inadequacy of the number of layers in the network structure. In this paper, a novel multi-spectral demosaicing method based on a deep convolution neural network (CNN) is proposed for the reconstruction of full-resolution multi-spectral images from raw MSFA-based spectral mosaic images. The CNN is integrated with the channel attention mechanism to protect important channel features. We verify the merits of the proposed method using 5 × 5 raw mosaic images on synthetic as well as real-world data. The experimental results show that the proposed method outperforms the existing demosaicing methods in terms of spatial details and spectral fidelity.