Remote Sensing Image Processing Research Articles

Spatial data mining-focused research on image processing techniques for remotely sensed images

ABSTRACT Spatial data mining is an important approach for collecting useful data from big datasets, especially remotely sensed images. This study tackles issues in environmental monitoring and management using sophisticated image processing. The Horse Herd Optimization-based VGG19 (HHO-VGG19) is proposed to improve land cover classification, recognition of objects, detection of changes, and detection of anomalies. The study used the BCDD dataset, which was scaled to 512 × 512 pixels, then applied Z-score normalization and extracted features using Principal Component Analysis (PCA). The VGG19 architecture was enhanced by utilizing Horse Herd Optimization to enhance image classification efficiency. The HHO-VGG19 model surpasses conventional techniques, with F1-score of 92%, a recall of 94%, an accuracy of 98.5%, and a 30-second execution time reduction. The findings indicate the efficiency of integrating sophisticated image processing with spatial data mining, giving an effective tool for remote sensing image processing in environmental uses including tracking ecosystems and handling of natural resources.

A Multi-Stage Progressive Pansharpening Network Based on Detail Injection with Redundancy Reduction.

In the field of remote sensing image processing, pansharpening technology stands as a critical advancement. This technology aims to enhance multispectral images that possess low resolution by integrating them with high-spatial-resolution panchromatic images, ultimately producing multispectral images with high resolution that are abundant in both spatial and spectral details. Thus, there remains potential for improving the quality of both the spectral and spatial domains of the fused images based on deep-learning-based pansharpening methods. This work proposes a new method for the task of pansharpening: the Multi-Stage Progressive Pansharpening Network with Detail Injection with Redundancy Reduction Mechanism (MSPPN-DIRRM). This network is divided into three levels, each of which is optimized for the extraction of spectral and spatial data at different scales. Particular spectral feature and spatial detail extraction modules are used at each stage. Moreover, a new image reconstruction module named the DRRM is introduced in this work; it eliminates both spatial and channel redundancy and improves the fusion quality. The effectiveness of the proposed model is further supported by experimental results using both simulated data and real data from the QuickBird, GaoFen1, and WorldView2 satellites; these results show that the proposed model outperforms deep-learning-based methods in both visual and quantitative assessments. Among various evaluation metrics, performance improves by 0.92-18.7% compared to the latest methods.

Multimodal self-supervised learning for remote sensing data land cover classification

Deep learning has revolutionized the remote sensing image processing techniques over the past few years. Nevertheless, annotating high-quality samples is difficult and time-consuming, which limits the performance of deep neural networks because of insufficient supervision information. Aiming to solve this contradiction, we investigate the multimodal self-supervised learning (MultiSSL) paradigm for pre-training and classification of remote sensing image. Specifically, the proposed self-supervised feature learning model consists of asymmetric encoder–decoder structure, in which deep unified encoder learns high-level key information characterizing multimodal remote sensing data and task-specific lightweight decoders are developed to reconstruct original data. To further enhance feature extraction capability, the cross-attention layers are utilized to exchange information contained in heterogeneous characteristics, thus learning more complementary information from multimodal remote sensing data. In fine-tuning stage, the pre-trained encoder and cross-attention layer serve as feature extractor, and leaned characteristics are combined with corresponding spectral information for land cover classification through a lightweight classifier. The self-supervised pre-training model can learn high-level key features from unlabeled samples, thereby utilizing the feature extraction capability of deep neural networks while reducing their dependence on annotated samples. Compared with existing classification paradigms, the proposed multimodal self-supervised pre-training and fine-tuning scheme achieves superior performance for remote sensing image land cover classification.

Deep Neural Remote Sensing and Sentinel-2 Satellite Image Processing of Kirkuk City, Iraq for Sustainable Prospective

Deep Neural Remote Sensing and Sentinel-2 Satellite Image Processing of Kirkuk City, Iraq for Sustainable Prospective

Land Cover Classification of Remote Sensing Imagery with Hybrid Two-Layer Attention Network Architecture

In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects.

Multi-View Feature Fusion and Rich Information Refinement Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation is currently a hot topic in remote sensing image processing. There are extensive applications in land planning and surveying. Many current studies combine Convolutional Neural Networks (CNNs), which extract local information, with Transformers, which capture global information, to obtain richer information. However, the fused feature information is not sufficiently enriched and it often lacks detailed refinement. To address this issue, we propose a novel method called the Multi-View Feature Fusion and Rich Information Refinement Network (MFRNet). Our model is equipped with the Multi-View Feature Fusion Block (MAFF) to merge various types of information, including local, non-local, channel, and positional information. Within MAFF, we introduce two innovative methods. The Sliding Heterogeneous Multi-Head Attention (SHMA) extracts local, non-local, and positional information using a sliding window, while the Multi-Scale Hierarchical Compressed Channel Attention (MSCA) leverages bar-shaped pooling kernels and stepwise compression to obtain reliable channel information. Additionally, we introduce the Efficient Feature Refinement Module (EFRM), which enhances segmentation accuracy by interacting the results of the Long-Range Information Perception Branch and the Local Semantic Information Perception Branch. We evaluate our model on the ISPRS Vaihingen and Potsdam datasets. We conducted extensive comparison experiments with state-of-the-art models and verified that MFRNet outperforms other models.

Destriping model for adaptive removal of arbitrary oriented stripes in remote sensing images

Abstract Destriping in remote sensing image processing remains a challenging problem, particularly when dealing with stripe noise of arbitrary orientations. Conventional methods struggle to eliminate oblique stripes, leaving a crucial gap in the production of higher-level remote sensing products. In response, we propose a novel destriping model, the Adaptive Stripe Noise Removal (ASNR) Method, designed to adapt to different orientations of stripe noise, aiming for accuracy, robustness, speed, and simplicity. The paper first addresses the conventional challenges in stripe removal, emphasizing the unintended loss of information during the process. To overcome this, we conduct a detailed study of stripe noise characteristics, employing traditional Fast Fourier Transform (FFT) for stripe orientation approximation. However, conventional techniques using spatial representations risk damaging detailed structures. To go beyond these limitations, the proposed method combines spectral processing technology with an image guidance mechanism. This approach aims to generate a guided image that retains both denoised features and important details. In the frequency domain, the method corrects the stripe image by estimating a guidance image. Experimental results, both qualitative and quantitative, demonstrate the superiority and stability of the proposed method in removing stripe noise and preserving image details without introducing artifacts. The novel approach fills a critical gap in destriping methods, offering a fast, accurate, and adaptable solution for arbitrary orientations of stripe noise in remote sensing images.

Noise-Disruption-Inspired Neural Architecture Search with Spatial–Spectral Attention for Hyperspectral Image Classification

In view of the complexity and diversity of hyperspectral images (HSIs), the classification task has been a major challenge in the field of remote sensing image processing. Hyperspectral classification (HSIC) methods based on neural architecture search (NAS) is a current attractive frontier that not only automatically searches for neural network architectures best suited to the characteristics of HSI data, but also avoids the possible limitations of manual design of neural networks when dealing with new classification tasks. However, the existing NAS-based HSIC methods have the following limitations: (1) the search space lacks efficient convolution operators that can fully extract discriminative spatial–spectral features, and (2) NAS based on traditional differentiable architecture search (DARTS) has performance collapse caused by unfair competition. To overcome these limitations, we proposed a neural architecture search method with receptive field spatial–spectral attention (RFSS-NAS), which is specifically designed to automatically search the optimal architecture for HSIC. Considering the core needs of the model in extracting more discriminative spatial–spectral features, we designed a novel and efficient attention search space. The core component of this innovative space is the receptive field spatial–spectral attention convolution operator, which is capable of precisely focusing on the critical information in the image, thus greatly enhancing the quality of feature extraction. Meanwhile, for the purpose of solving the unfair competition issue in the traditional differentiable architecture search (DARTS) strategy, we skillfully introduce the Noisy-DARTS strategy. The strategy ensures the fairness and efficiency of the search process and effectively avoids the risk of performance crash. In addition, to further improve the robustness of the model and ability to recognize difficult-to-classify samples, we proposed a fusion loss function by combining the advantages of the label smoothing loss and the polynomial expansion perspective loss function, which not only smooths the label distribution and reduces the risk of overfitting, but also effectively handles those difficult-to-classify samples, thus improving the overall classification accuracy. Experiments on three public datasets fully validate the superior performance of RFSS-NAS.

SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images

One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification. Despite this, researchers continue to study and propose simpler, more robust models. This study presents a novel deep-learning approach, the iterative convolutional neural network (ICNN), which combines spectral–fractal features and classifier probability maps iteratively, aiming to enhance the HSI classification accuracy. Experiments are conducted to prove the accuracy enhancement of the proposed method using HSI benchmark datasets of Indian pine (IP) and the University of Pavia (PU) to evaluate the performance of the proposed technique. The final results show that the proposed approach reaches overall accuracies of 99.16% and 95.5% on the IP and PU datasets, respectively, which are better than some basic methods. Additionally, the end findings demonstrate that greater accuracy levels might be achieved using a primary CNN network that employs the iteration loop than with certain current state-of-the-art spatial–spectral HSI classification techniques.

Adaptive Image-Defogging Algorithm Based on Bright-Field Region Detection

Image defogging is an essential technology used in traffic safety monitoring, military surveillance, satellite and remote sensing image processing, medical image diagnostics, and other applications. Current methods often rely on various priors, with the dark-channel prior being the most frequently employed. However, halo and bright-field color distortion issues persist. To further improve image quality, an adaptive image-defogging algorithm based on bright-field region detection is proposed in this paper. Modifying the dark-channel image improves the abrupt changes in gray value in the traditional dark-channel image. By setting the first and second lower limits of transmittance and introducing an adaptive correction factor to adjust the transmittance of the bright-field region, the limitations of the dark-channel prior in extensive ranges and high-brightness areas can be significantly alleviated. In addition, a guide filter is utilized to enhance the initial transmittance image, preserving the details of the defogged image. The results of the experiment demonstrate that the algorithm presented in this paper effectively addresses the mentioned issues and has shown outstanding performance in both objective evaluation and subjective visual effects.

BiF-DETR:Remote sensing object detection based on Bidirectional information fusion

Remote Sensing Object Detection(RSOD) is a fundamental task in the field of remote sensing image processing. The complexity of the background, the diversity of object scales and the locality limitation of Convolutional Neural Network (CNN) present specific challenges for RSOD. In this paper, an innovative hybrid detector, Bidirectional Information Fusion DEtection TRansformer (BiF-DETR), is proposed to mitigate the above issues. Specifically, BiF-DETR takes anchor-free detection network, CenterNet, as the baseline, designs the feature extraction backbone in parallel, extracts the local feature details using CNNs, and obtains the global information and long-term dependencies using Transformer branch. A Bidirectional Information Fusion (BIF) module is elaborately designed to reduce the semantic differences between different styles of feature maps through multi-level iterative information interactions, fully utilizing the complementary advantages of different detectors. Additionally, Coordination Attention(CA), is introduced to enables the detection network to focus on the saliency information of small objects. To address diversity insufficiency of remote sensing images in the training stage, Cascade Mixture Data Augmentation (CMDA), is designed to improve the robustness and generalization ability of the model. Comparative experiments with other cutting-edge methods are conducted on the publicly available DOTA and NWPU VHR-10 datasets. The experimental results reveal that the performance of proposed method is state-of-the-art, with mAP reaching 77.43% and 94.75%, respectively, far exceeding the other 25 competitive methods.

Attention Guide Axial Sharing Mixed Attention (AGASMA) Network for Cloud Segmentation and Cloud Shadow Segmentation

Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation.

A Prior-Guided Dual Branch Multi-Feature Fusion Network for Building Segmentation in Remote Sensing Images

The domain of remote sensing image processing has witnessed remarkable advancements in recent years, with deep convolutional neural networks (CNNs) establishing themselves as a prominent approach for building segmentation. Despite the progress, traditional CNNs, which rely on convolution and pooling for feature extraction during the encoding phase, often fail to precisely delineate global pixel interactions, potentially leading to the loss of vital semantic details. Moreover, conventional CNN-based segmentation models frequently neglect the nuanced semantic differences between shallow and deep features during the decoding phase, which can result in subpar feature integration through rudimentary addition or concatenation techniques. Additionally, the unique boundary characteristics of buildings in remote sensing images, which offer a rich vein of prior information, have not been fully harnessed by traditional CNNs. This paper introduces an innovative approach to building segmentation in remote sensing images through a prior-guided dual branch multi-feature fusion network (PDBMFN). The network is composed of a prior-guided branch network (PBN) in the encoding process, a parallel dilated convolution module (PDCM) designed to incorporate prior information, and a multi-feature aggregation module (MAM) in the decoding process. The PBN leverages prior region and edge information derived from superpixels and edge maps to enhance edge detection accuracy during the encoding phase. The PDCM integrates features from both branches and applies dilated convolution across various scales to expand the receptive field and capture a more comprehensive semantic context. During the decoding phase, the MAM utilizes deep semantic information to direct the fusion of features, thereby optimizing segmentation efficacy. Through a sequence of aggregations, the MAM gradually merges deep and shallow semantic information, culminating in a more enriched and holistic feature representation. Extensive experiments are conducted across diverse datasets, such as WHU, Inria Aerial, and Massachusetts, revealing that PDBMFN outperforms other sophisticated methods in terms of segmentation accuracy. In the key segmentation metrics, including mIoU, precision, recall, and F1 score, PDBMFN shows a marked superiority over contemporary techniques. The ablation studies further substantiate the performance improvements conferred by the PBN’s prior information guidance and the efficacy of the PDCM and MAM modules.

Micro-Scale Analysis and Optimization of Rural Settlement Spatial Patterns: A Case Study of Huanglong Town, Dayu County

Optimizing the spatial patterns of rural settlements is crucial for rural development and revitalization. Enhancing the internal spatial configuration of these settlements necessitates a comprehensive understanding of their micro-scale spatial characteristics. This study develops evaluation indicators and methodologies to quantify rural settlement spatial patterns by analyzing their multidimensional aspects. The research utilizes Huanglong Town in Dayu County, Jiangxi Province, as a case study for exploring micro-scale spatial patterns and proposing corresponding optimization models. The research employs remote sensing image processing and GIS spatial analysis to collect data on the study area. The results indicate that rural settlements in Huanglong Town generally form clustered patterns with moderate spatial structure intensity and order. Notably, spatial heterogeneity is observed across the northern mountainous area, the central plain and low hilly region, and the southern hilly area. Based on these findings, the study categorizes rural settlements in Huanglong Town into four optimization models: stable improvement, internal potential exploitation, controlled expansion, and relocation and withdrawal. Each model is associated with differentiated optimization strategies. By integrating analyses of spatial form, structure, and order, this study reveals the intrinsic spatial characteristics of rural settlements, offering a systematic approach to guide their spatial optimization.

Restoration of Semantic-Based Super-Resolution Aerial Images

Currently, technologies for remote sensing image processing are actively developing, including both satellite images and aerial images obtained from video cameras of unmanned aerial vehicles. Often such images have artifacts such as low resolution, blurred image fragments, noise, etc. One way to overcome such limitations is to use modern technologies to restore super-resolution images based on deep learning methods. The specificity of aerial images is the presentation of texture and structural elements in a higher resolution than in satellite images, which objectively contributes to better results of restoration. The article provides a classification of super-resolution methods based on the main architectures of deep neural networks, namely convolutional neural networks, visual transformers and generative adversarial networks. The article proposes a method for reconstructing super-resolution aerial images SemESRGAN taking into account semantic features by using an additional deep network for semantic segmentation during the training stage. The total loss function, including adversarial losses, pixel-level losses, and perception losses (feature similarity), is minimized. Six annotated aerial and satellite image datasets CLCD, DOTA, LEVIR-CD, UAVid, AAD, and AID were used for the experiments. The results of image restoration using the proposed SemESRGAN method were compared with the basic architectures of convolutional neural networks, visual transformers and generative adversarial networks. Comparative results of image restoration were obtained using objective metrics PSNR and SSIM, which made it possible to evaluate the quality of restoration using various deep network models.

Multi-Branch Attention Fusion Network for Cloud and Cloud Shadow Segmentation

In remote sensing image processing, the segmentation of clouds and their shadows is a fundamental and vital task. For cloud images, traditional deep learning methods often have weak generalization capabilities and are prone to interference from ground objects and noise, which not only results in poor boundary segmentation but also causes false and missed detections of small targets. To address these issues, we proposed a multi-branch attention fusion network (MAFNet). In the encoder section, the dual branches of ResNet50 and the Swin transformer extract features together. A multi-branch attention fusion module (MAFM) uses positional encoding to add position information. Additionally, multi-branch aggregation attention (MAA) in the MAFM fully fuses the same level of deep features extracted by ResNet50 and the Swin transformer, which enhances the boundary segmentation ability and small target detection capability. To address the challenge of detecting small cloud and shadow targets, an information deep aggregation module (IDAM) was introduced to perform multi-scale deep feature aggregation, which supplements high semantic information, improving small target detection. For the problem of rough segmentation boundaries, a recovery guided module (RGM) was designed in the decoder section, which enables the model to effectively allocate attention to complex boundary information, enhancing the network’s focus on boundary information. Experimental results on the Cloud and Cloud Shadow dataset, HRC-WHU dataset, and SPARCS dataset indicate that MAFNet surpasses existing advanced semantic segmentation techniques.

PDE-Constrained Scale Optimization Selection for Feature Detection in Remote Sensing Image Matching

Feature detection and matching is the key technique for remote sensing image processing and related applications. In this paper, a PDE-constrained optimization model is proposed to determine the scale levels advantageous for feature detection. A variance estimation technique is introduced to treat the observation optical images polluted by additive zero-mean Gaussian noise and determine the parameter of a nonlinear scale space governed by the partial differential equation. Additive Operator Splitting is applied to efficiently solve the PDE constraint, and an iterative algorithm is proposed to approximate the optimal subset of the original scale level set. The selected levels are distributed more uniformly in the total variation sense and helpful for generating more accurate and robust feature points. The experimental results show that the proposed method can achieve about a 30% improvement in the number of correct matches with only a small increase in time cost.

Theoretical Framework Construction of Remote Sensing Image Processing Technology in The Study of Urban Heat Island Effect

This study aims to construct a theoretical framework to discuss the application of remote sensing image processing technology in the study of urban heat island effect. Urban heat island effect refers to the phenomenon that the temperature of urban area is significantly higher than that of surrounding rural area, which has become an important problem in the process of global climate change and urbanization. Remote sensing technology provides a powerful tool for in-depth understanding and effective response to this phenomenon. This study first reviews the existing remote sensing image processing technologies, including multispectral and hyperspectral imaging, thermal infrared imaging, laser radar (LiDAR), and analyzes the application status of these technologies in the study of urban heat island effect. Then, this paper puts forward a comprehensive theoretical framework, combining the key steps of remote sensing data acquisition, image preprocessing, feature extraction, classification and segmentation, and change detection, systematically describes how to use remote sensing image processing technology to monitor and analyze urban heat island effect. In the research process, this paper adopts the methods of literature review and case analysis, and extracts the core elements and key technologies of remote sensing image processing technology in the study of urban heat island effect by systematically combing the relevant research results at home and abroad. The research results show that remote sensing image processing technology has significant advantages in the spatial distribution characteristics, time change rules, and analysis of influencing factors of urban heat island effect, and can provide high-precision and high-timeliness monitoring data, and provide scientific basis for urban planning and environmental management. The theoretical framework of this paper provides guidance for future empirical research, and provides a reference for policy makers and urban managers, which has important academic value and practical application significance.

AFMUNet: Attention Feature Fusion Network Based on a U-Shaped Structure for Cloud and Cloud Shadow Detection

Cloud detection technology is crucial in remote sensing image processing. While cloud detection is a mature research field, challenges persist in detecting clouds on reflective surfaces like ice, snow, and sand. Particularly, the detection of cloud shadows remains a significant area of concern within cloud detection technology. To address the above problems, a convolutional self-attention mechanism feature fusion network model based on a U-shaped structure is proposed. The model employs an encoder–decoder structure based on UNet. The encoder performs down-sampling to extract deep features, while the decoder uses up-sampling to reconstruct the feature map. To capture the key features of the image, Channel Spatial Attention Module (CSAM) is introduced in this work. This module incorporates an attention mechanism for adaptive field-of-view adjustments. In the up-sampling process, different channels are selected to obtain rich information. Contextual information is integrated to improve the extraction of edge details. Feature fusion at the same layer between up-sampling and down-sampling is carried out. The Feature Fusion Module (FFM) facilitates the positional distribution of the image on a pixel-by-pixel basis. A clear boundary is distinguished using an innovative loss function. Finally, the experimental results on the dataset GF1_WHU show that the segmentation results of this method are better than the existing methods. Hence, our model is of great significance for practical cloud shadow segmentation.

MRFA-Net: Multi-Scale Receptive Feature Aggregation Network for Cloud and Shadow Detection

The effective segmentation of clouds and cloud shadows is crucial for surface feature extraction, climate monitoring, and atmospheric correction, but it remains a critical challenge in remote sensing image processing. Cloud features are intricate, with varied distributions and unclear boundaries, making accurate extraction difficult, with only a few networks addressing this challenge. To tackle these issues, we introduce a multi-scale receptive field aggregation network (MRFA-Net). The MRFA-Net comprises an MRFA-Encoder and MRFA-Decoder. Within the encoder, the net includes the asymmetric feature extractor module (AFEM) and multi-scale attention, which capture diverse local features and enhance contextual semantic understanding, respectively. The MRFA-Decoder includes the multi-path decoder module (MDM) for blending features and the global feature refinement module (GFRM) for optimizing information via learnable matrix decomposition. Experimental results demonstrate that our model excelled in generalization and segmentation performance when addressing various complex backgrounds and different category detections, exhibiting advantages in terms of parameter efficiency and computational complexity, with the MRFA-Net achieving a mean intersection over union (MIoU) of 94.12% on our custom Cloud and Shadow dataset, and 87.54% on the open-source HRC_WHU dataset, outperforming other models by at least 0.53% and 0.62%. The proposed model demonstrates applicability in practical scenarios where features are difficult to distinguish.