Multi-modality image fusion aims to combine different modalities produce fused images that retain the complementary features of each modality, such as functional highlights and texture details. To leverage strong generative priors address challenges unstable training lack interpretability for GAN-based methods, we propose a novel algorithm based on denoising diffusion probabilistic model (DDPM). The task is formulated conditional generation problem under DDPM sampling framework, which...

Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial spectral information of source images into a fused one, which has higher resolution more reliable for downstream tasks compared with any images. It been widely applied interpretation pre-processing various applications. A large number methods have proposed achieve better fusion results by considering relationships among panchromatic In recent years, fast development artificial intelligence (AI) deep...

Semantic Change Detection (SCD) refers to the task of simultaneously extracting changed areas and semantic categories (before after changes) in Remote Sensing Images (RSIs). This is more meaningful than Binary (BCD) since it enables detailed change analysis observed areas. Previous works established triple-branch Convolutional Neural Network (CNN) architectures as paradigm for SCD. However, remains challenging exploit information with a limited amount samples. In this work, we investigate...

Hyperspectral (HS) and multispectral (MS) image fusion aims at producing high-resolution HS (HRHS) images. However, the existing methods could not simultaneously consider structures in both spatial spectral domains of cube. In order to effectively preserve spatial–spectral HRHS images, we propose a new low-resolution (LRHS) MS (HRMS) method based on spatial–spectral-graph-regularized low-rank tensor decomposition (SSGLRTD) this paper. First, reformulate problem as model utilize property...

Fusing low spatial resolution hyperspectral (LRHS) images and high multispectral (HRMS) to obtain (HRHS) has received increasing interests in recent years. In this paper, a new group spectral embedding (GSE)-based LRHS HRMS image fusion method is proposed by exploring the multiple manifold structures of bands low-rank structure HRHS data. First, factorization (LRFF)-based robust recovery model developed for images, regarding as degradation sparse prior difference images. Then, an assumption...

In this article, we present a new pansharpening method, zero-reference generative adversarial network (ZeRGAN), which fuses low spatial resolution multispectral (LR MS) and high panchromatic (PAN) images. the proposed indicates that it does not require paired reduced-scale images or unpaired full-scale for training. To obtain accurate fusion results, establish an game between set of multiscale generators their corresponding discriminators. Through generators, fused MS (HR are progressively...

Thanks to their capability of modeling global information, transformers have been recently applied change detection in remote sensing images. Generally, the changes terms shape and appearance objects lead relation among these multi-temporal However, this context, attention mechanism has not fully explored yet learn observed scenes. In paper, we analyze images propose a cross-temporal difference (CTD) capture efficiently. Through CTD attention, changed areas are distinguished better from...

Finding accurate injection components is the key issue in pan-sharpening methods. In this paper, a low-rank (LRP) model developed from new perspective of offset learning. Two offsets are defined to represent spatial and spectral differences between low-resolution multispectral high-resolution (HRMS) images, respectively. order reduce distortions, equalization proportion constraints designed cast on offsets, develop constrained stable decomposition algorithm via augmented Lagrange multiplier....

Deep unfolding networks have obtained satisfactory performance in the pansharpening task owing to their sufficient interpretability. Inspired by back-projection (BP) mechanism, we propose a BP-driven model, spatial-spectral dual back-project network (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DBPN), fuse low spatial resolution multispectral (LR MS) and high panchromatic (PAN) images exploiting BP spectral domains. Specifically,...

The integration of spatial and spectral information is beneficial to the improvement change detection (CD) performance. However, existing methods cannot efficiently suppress influences differences (SDs) in unchanged areas. To address these issues, this article, we propose a content-guided spatial–spectral network (CSI-Net) for fusion global details SD information. Specifically, proposed CSI-Net composed reasoning (SR) module, an (CGI) module. In SR learned by cascaded graph convolution (GC)...

In recent years, with the rapid development of unmanned aerial vehicle (UAV), images have extended across various industries such as intelligent building, agriculture, transportation, and Industry 4.0. Notably, security UAV‐assisted data acquisition during transmission has become a critical concern. The reversible hiding (RDH) method can hide in for ensure secure communication. general, an image may exhibit substantially different orientation regularity from natural scene image. This casts...

Pansharpening methods based on deep neural networks (DNNs) have been attracting great attention due to their powerful representation capabilities. In this article, combine the feature maps from different subnetworks efficiently, we propose a novel pansharpening method spatial and spectral extraction network (SSE-Net). Different other DNNs that directly concatenate features subnetworks, design adaptive fusion modules (AFFMs) merge these according information content. First, are extracted by...

Pan-sharpening methods based on deep neural network (DNN) have produced the state-of-the-art results. However, common information in panchromatic (PAN) image and low spatial resolution multispectral (LRMS) is not sufficiently explored. As PAN LRMS images are collected from same scene, there exists some among them, addition to their respective unique information. The direct concatenation of extracted features leads redundancy feature space. To reduce exploit global source images, we proposed...

Recently, deep neural network (DNN)-based methods have achieved good results in terms of the fusion low spatial resolution hyperspectral (LR HS) and high multispectral (HR MS) images. However, spectral band correlation (SBC) nonlocal similarity (SNS) (HS) images are not sufficiently exploited by them. To model two priors efficiently, we propose a spectral-spatial dual graph unfolding (SDGU-Net), which is derived from optimization regularized restoration models. Specifically, introduce graphs...

Fusion of a panchromatic (PAN) image and corresponding multispectral (MS) is also known as pansharpening, which aims to combine abundant spatial details PAN spectral information MS images. Due the absence high-resolution images, available deep-learning-based methods usually follow paradigm training at reduced resolution testing both full resolution. When taking original images inputs, they always obtain sub-optimal results due scale variation. In this paper, we propose explore...

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Recently, sparse coding-based image fusion methods have been developed extensively. Although most of them can produce competitive results, three issues need to be addressed: 1) these divide the into overlapped patches and process independently, which ignore consistency pixels in patches; 2) partition strategy results loss spatial structures for entire image; 3) correlation bands multispectral (MS) is ignored. In this paper, we propose a novel method based on convolution structure coding...

In this paper, we construct a new coupled sparse non-negative matrix factorization (CSNMF) model for the fusion of panchromatic (PAN) and multispectral (MS) images. Two CSNMFs are developed joint representation MS PAN Moreover, sequential iterative algorithm is proposed to simultaneously find solution CSNMF. Because learned dictionaries can reveal latent structure images in spatial spectral domains, fused high-resolution be calculated by multiplying dictionary image coefficients Some...

Pan-sharpening methods based on deep neural network (DNN) have produced state-of-the-art fusion performance. However, DNN-based mainly focus the modeling of local properties in low spatial resolution multispectral (LR MS) and panchromatic (PAN) images by convolution networks. The global dependencies are ignored. To capture concurrently, we propose a multiscale spatial–spectral interaction transformer (MSIT) for pan-sharpening. Specifically, construct sub-networks containing...

In this paper a self-paced learning-based probability subspace projection (SL-PSP) method is proposed for hyperspectral image classification. First, label assigned each pixel, and risk labeled pixel. Then, two regularizers are developed from maximum margin graph, respectively. The first regularizer can increase the discriminant ability of features by gradually involving most confident pixels into to simultaneously push away heterogeneous neighbors pull inhomogeneous neighbors. second adopts...

Pansharpening aims to obtain high spatial resolution multispectral (MS) images by fusing the and spectral information in low (LR) MS panchromatic (PAN) images. Recently, deep neural network (DNN) based pansharpening methods have been advanced extensively. Although most DNN-based show good performance, it is difficult for them preserve details fused image. In this article, we propose a new method on triplet attention with interaction efficiently enhance First, different mechanisms are...