Hyperspectral remote sensing images capture detailed spectral characteristics of features, while multispectral remote sensing images provide clear spatial distribution. Fusing these two types of images can enhance feature identification and classification accuracy. Current deep learning algorithms achieve high fusion quality but struggle with balancing global effective perception and lightweight computation. Moreover, these algorithms typically discretely handle data mapping, which contrasts with the continuous nature of the world. Recently, the Mamba has shown significant potential for complex long-range modelling, addressing the computational complexity of global perception. Concurrently, Implicit Neural Representation (INR) offers high-quality solutions for continuous domain modelling. To this end, this study introduces a novel network architecture that combines Mamba and INR, termed the Mamba Cooperative INR Fusion Network (MCIFNet). MCIFNet effectively captures global image information and generates fused images in a continuous domain through point-to-point processing. The network comprises two main units: potential space projection and semantic extraction and fusion. The potential space projection unit performs shallow encoding of hyperspectral and multispectral remote sensing images, mapping them to a latent feature space. The semantic extraction and fusion unit utilizes Scale Adaptive Residual State Spatial and Implicit Spatial-Spectral Fusion modules to extract deep features from the bi-modal images, generating fused images point-by-point. A series of fusion experiments with 4x, 8x, and 16x scale factors demonstrate that MCIFNet surpasses popular algorithms in both spatial detail and spectral information reconstruction, while also providing more lightweight performance. The code for MCIFNet will be shared on https://github.com/chunyuzhu/MCIFNet.

Multispectral and Hyperspectral Image Fusion (MHIF) involves the fusion of high spatial resolution multispectral images (HR-MSI) and low spatial resolution hyperspectral images (LR-HSI) to generate high spatial resolution hyperspectral images (HR-HSI), has gained significant attention in the field of remote sensing imaging. While CNN and Transformer models have shown effectiveness in MHIF, existing CNN or Transformer-based algorithms are overburdened with model size, making it difficult to achieve an effective trade-off between fusion accuracy and degree of lightweight. Recently, Implicit Neural Representation (INR) has been proven good interpretability and the ability to exploit coordinate information in 2D tasks. Nonetheless, INR-based fusion networks have certain limitations, such as the need for deeper super-resolution networks as shallow encoders, and insufficient representation capability on high upsampling ratios. To address these challenges, we present the Quadtree Implicit Sampling (QIS), which employs a hierarchical sampling from the perspective of the quadtree, to enhance the capacity of the overall network. Furthermore, the remarkable design of QIS allows us to adopt a lightweight structure as the shallow encoder, greatly alleviating the network burden and achieving lightweight. Inspired by generative adversarial models, we incorporate QIS as a lightweight generator into the GAN framework named QIS-GAN and leverage a discriminator to increase the fidelity of fused images. The results showcase the superior performance of QIS-GAN on the MHIF tasks with upsampling ratios of ×4, ×8, and ×16, surpassing the state-of-the-art in several datasets. The code for our approach will be available at https://github.com/chunyuzhu/QIS-GAN.

Hyperspectral and multispectral remote sensing image fusion is an optimal approach for generating hyperspectral–spatial-resolution images, effectively overcoming the physical limitations of sensors. In transformer-based image fusion methods constrained by the local window self-attention mechanism, the extraction of global information and coordinated contextual features is often insufficient. Fusion that aims to emphasize spatial–spectral heterogeneous characteristics may significantly enhance the robustness of joint representation for multi-source data. To address these issues, this study proposes a hyperspectral and multispectral remote sensing image fusion method based on a retractable spatial–spectral transformer network (RSST) and introduces the attention retractable mechanism into the field of remote sensing image fusion. Furthermore, a gradient spatial–spectral recovery block is incorporated to effectively mitigate the limitations of token interactions and the loss of spatial–spectral edge information. A series of experiments across multiple scales demonstrate that RSST exhibits significant advantages over existing mainstream image fusion algorithms.

When the near-field and far-field information of a target is uncertain, it is necessary to choose a suitable localization method. The modified polar representation (MPR) method integrates the two scenarios and achieves a unified localization with direction of arrival (DOA) estimation in the far field and position estimation in the near field. Previous studies have only proposed solutions for stationary environments and have not considered the motion factor. Therefore, this paper proposes a new unified positioning algorithm using multi-sensor time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements without prior target source information. The method represents the position of the target source using MPR and describes the localization problem as a weighted least squares (WLS) problem with two constraints. We first obtain the initial estimates by WLS without considering the constraints and then investigate a two-step error correction method based on the constraints. The first step corrects the initial estimate using the Taylor series expansion technique, and the second step corrects the DOA estimate in the previous step using the direct error compensation technique based on the properties of the second constraint. Simulation experiments show that the method is effective for the unified positioning of moving targets and can achieve the Cramer–Rao lower bound (CRLB).