Remote Sensing Image Processing Research Articles

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.

Application of Wireless Network Remote Sensing Image and Video Processing Technology in Smart City Design and planning

With the rapid development of society, regional urban construction has become the main guarantee of economic development, and smart city construction has become the main trend of regional development. Smart cities consist of information images and urban development, which can meet the needs of the modern generation for material life, knowledge and scientific life. Therefore, wireless remote sensing (RS) and video image processing have become key tools for the development of smart cities, which can solve the problems encountered by many professionals before, and become an integral part of urban construction. On this basis, this paper summarized the video processing image and wireless network RS image, and analyzed their development and application in urban design and layout. Based on this, this paper expounded the application of wireless network RS image in smart city design and layout, and analyzed the application prospects of RS technology in building smart city space-time information framework, RS+urban management, RS+smart environmental protection, RS+smart city agriculture and video processing technology in smart city design and planning. It analyzed the application in intelligent security, intelligent transportation and management visualization, and then used the two-dimensional discrete Fourier transform (2D-DFT) definition to strengthen the design and layout of smart cities. According to experiments and surveys, introducing 2D-DFT into the design and layout of smart cities can build a more intelligent city and improve residents’ satisfaction by 30%.

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment’s findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

Study on heavy metal content calculation and agricultural potential based on hyperspectral remote sensing image processing

Study on heavy metal content calculation and agricultural potential based on hyperspectral remote sensing image processing

Remote sensing image classification method based on improved ShuffleNet convolutional neural network

As a list of remotely sensed data sources is available, the effective processing of remote sensing images is of great significance in practical applications in various fields. This paper proposes a new lightweight network to solve the problem of remote sensing image processing by using the method of deep learning. Specifically, the proposed model employs ShuffleNet V2 as the backbone network, appropriately increases part of the convolution kernels to improve the classification accuracy of the network, and uses the maximum overlapping pooling layer to enhance the detailed features of the input images. Finally, Squeeze and Excitation (SE) blocks are introduced as the attention mechanism to improve the architecture of the network. Experimental results based on several multisource data show that our proposed network model has a good classification effect on the test samples and can achieve more excellent classification performance than some existing methods, with an accuracy of 91%, and can be used for the classification of remote sensing images. Our model not only has high accuracy but also has faster training speed compared with large networks and can greatly reduce computation costs. The demo code of our proposed method will be available at https://github.com/li-zi-qi.

Neural network big data fusion in remote sensing image processing technology

Abstract Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks.

ASPP+-LANet: A Multi-Scale Context Extraction Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Semantic segmentation of remote sensing (RS) images is a pivotal branch in the realm of RS image processing, which plays a significant role in urban planning, building extraction, vegetation extraction, etc. With the continuous advancement of remote sensing technology, the spatial resolution of remote sensing images is progressively improving. This escalation in resolution gives rise to challenges like imbalanced class distributions among ground objects in RS images, the significant variations of ground object scales, as well as the presence of redundant information and noise interference. In this paper, we propose a multi-scale context extraction network, ASPP+-LANet, based on the LANet for semantic segmentation of high-resolution RS images. Firstly, we design an ASPP+ module, expanding upon the ASPP module by incorporating an additional feature extraction channel, redesigning the dilation rates, and introducing the Coordinate Attention (CA) mechanism so that it can effectively improve the segmentation performance of ground object targets at different scales. Secondly, we introduce the Funnel ReLU (FReLU) activation function for enhancing the segmentation effect of slender ground object targets and refining the segmentation edges. The experimental results show that our network model demonstrates superior segmentation performance on both Potsdam and Vaihingen datasets, outperforming other state-of-the-art (SOTA) methods.

OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images

Object detection is a fundamental task of remote-sensing image processing. Most existing object detection detectors handle regression and classification tasks through learning from a fixed set of learnable anchors or queries. To simplify object candidates, we propose a denoising diffusion process for remote-sensing image object detection, which directly detects objects from a set of random boxes. During the training phase, the horizontal detection boxes are transformed into oriented detection boxes firstly. Then, the model learns to reverse this transformation process by diffusing from the ground truth-oriented box to a random distribution. During the inference phase, the model incrementally refines a set of randomly generated boxes to produce the final output result. Remarkable results have been achieved using our proposed method. For instance, on commonly used object detection datasets such as DOTA, our approach achieves a mean average precision (mAP) of 76.59%. Similarly, on the HRSC2016 dataset, our method achieves a 72.4% mAP.

A Siamese Swin-Unet for image change detection

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying).

Analysis of land use/land cover changes and driving forces during the period 1992–2022: a case study of Jinan City, China

Abstract This research aims to quantify the spatial pattern of urban land use/land cover (LULC) change while considering environmental effects. This paper integrates historical Landsat imagery, The Environment for Visualizing Images (ENVI), geographical information system (GIS), and socioeconomic data to determine the spatial–temporal urban LULC dynamics and the conversion of LULC in response to the rapid urbanization from 1992 to 2022. Principle component analysis and multiple linear regression are used to determine and model the relationship between the socioeconomic factors and the changes for identifying the driving forces. The results indicate that impervious surfaces have exponentially increased, expanding more than two times from 2,348 to 4,795 km2, in contrast to bare lands, which drastically declined by 95%, from 1,888 to 87 km2. Water bodies have always been relatively fewer, at approximately 100 km2. In addition, the majority of farmland in Jinan City is concentrated in the northern region with a steady area in the range of 2,100–2,900 km2, while the majority of woodland located in the southern region declined from 3,774.52 km2 (37%) to 3,088.28 km2 (30%). Economic development, population growth, and climate change are the primary factors that have an obvious impact on LULC changes.

Transformer-based dual path cross fusion for pansharpening remote sensing images

ABSTRACT The purpose of pan-sharpening is to generate high-resolution multispectral (HRMS) images by combining multispectral images (MS) and panchromatic images (PAN), which have become an important part of remote sensing image processing. Therefore, how to better extract complete feature information from MS images and PAN images has become the focus of our attention. In this paper, we propose a new pansharpening method, called Transformer-based Dual-path cross fusion network (TDF) for Pan-sharpening remote sensing images, which aims to extract the spatial details of PAN images while maintaining the spectral fidelity of MS images. The whole network structure can be divided into two parts: in the encoder part, we adopt the Swin-Transformer module for the downsampling operation, which expands the sensory field of the network to the feature map, and then extracts the global information. However, since the Swin-Transformer module is not good at extracting pixel-level details, we introduce the Base Feature Extraction (BFE) and the Invertible Neural Network Block (INNB) modules for the interaction between the local and the global feature information. we also introduce the Edge-Enhancement Block (EEB) to further enhance the feature extraction at multi-scales during the image fusion process. In the decoder section, we once again employ the Swin Transformer module for downsampling. After the convolution operation and activation function, we utilize the Sub-Pixel Convolutional Neural Network for upsampling to generate the ultimate high-resolution multispectral images. Simulation experiments and real experiments are conducted on QuickBird (QB) and WorldView2 (WV2) datasets, which demonstrated our method are superior to the current methods.

Retracted: Application and Analysis of Remote Sensing Image Processing Technology in Robotic Power Inspection

Retracted: Application and Analysis of Remote Sensing Image Processing Technology in Robotic Power Inspection

Multispectral Object Detection Based on Multilevel Feature Fusion and Dual Feature Modulation

Multispectral object detection is a crucial technology in remote sensing image processing, particularly in low-light environments. Most current methods extract features at a single scale, resulting in the fusion of invalid features and the failure to detect small objects. To address these issues, we propose a multispectral object detection network based on multilevel feature fusion and dual feature modulation (GMD-YOLO). Firstly, a novel dual-channel CSPDarknet53 network is used to extract deep features from visible-infrared images. This network incorporates a Ghost module, which generates additional feature maps through a series of linear operations, achieving a balance between accuracy and speed. Secondly, the multilevel feature fusion (MLF) module is designed to utilize cross-modal information through the construction of hierarchical residual connections. This approach strengthens the complementarity between different modalities, allowing the network to improve multiscale representation capabilities at a more refined granularity level. Finally, a dual feature modulation (DFM) decoupling head is introduced to enhance small object detection. This decoupled head effectively meets the distinct requirements of classification and localization tasks. GMD-YOLO is validated on three public visible-infrared datasets: DroneVehicle, KAIST, and LLVIP. DroneVehicle and LLVIP achieved mAP@0.5 of 78.0% and 98.0%, outperforming baseline methods by 3.6% and 4.4%, respectively. KAIST exhibited an MR of 7.73% with an FPS of 61.7. Experimental results demonstrated that our method surpasses existing advanced methods and exhibits strong robustness.

Spatiotemporal relation of satellite-based meteorological to agricultural drought in the downstream Citarum watershed, Indonesia

Jatiluhur dam, located in West Java province, is one of the most extensive reservoirs in Indonesia and is used to secure the water supply for 240,000 ha of rice fields to prevent agricultural drought in the downstream Citarum watershed. During the last two decades, several areas in the downstream Citarum watershed have been experiencing agricultural drought following meteorological drought events. This study aimed to examine the spatiotemporal patterns of agricultural drought and their correlation with meteorological drought occurrence by utilizing a remote sensing image processing technique. The Normalized Difference Drought Index (NDDI) and Standardized Precipitation Index (SPI) derived from MODIS and CHIRPS data were used to estimate agricultural and meteorological drought events, respectively. The results showed that the magnitude of agricultural drought in the downstream Citarum watershed was varied and related to earlier meteorological drought. The agricultural drought mainly occurred after a meteorological event with the magnitude mostly related to the magnitude of the ONI and DMI. A strong El Nino and positive IOD will cause a higher magnitude of agricultural drought. The time lag between the two drought occurrences was found to be related to the distance of the paddy fields from the main irrigation network and the proximity of the main irrigation network to the Jatiluhur Dam spillway.

The Effect of Vegetation Density on The Total Suspended Solid (TSS) Concentration in The Aquatic Area of Pomalaa District

Analysis of the influence of vegetation density levels on the concentration of Total Suspended Solids (TSS) in aquatic areas is crucial in the Pomalaa District, an area characterized by extensive mining activities. This research aims to examine changes in vegetation density conditions and analyze the influence of vegetation density levels on TSS concentrations in the aquatic region. Changes in vegetation density conditions are analyzed through multi-temporal remote sensing image processing using the NDVI method. The TSS concentration is analyzed using the gravimetric method. Meanwhile, the influence of vegetation density level on TSS concentration is analyzed using the Pearson correlation coefficient equation. The results of the research show that there has been an increase in the area with very low levels of vegetation density and a decrease in the area with high levels of vegetation density in the period 2014 to 2023. The calculated results of TSS concentrations at the research location are 78 mg/l to 111 mg/l. The correlation analysis shows that the broader the area with very low vegetation density, the higher the TSS concentration in the aquatic environment. Additionally, the larger the area with high vegetation density, the lower the TSS concentration value of the water at the research locations.

Remote sensing Image processing, DEEP learning Classification algorithms survey and review

Remote sensing Image processing, DEEP learning Classification algorithms survey and review

Editorial: Foreword to the Special Issue on Artificial Intelligence for Hyper- and Multispectral Remote Sensing Image Processing

Editorial: Foreword to the Special Issue on Artificial Intelligence for Hyper- and Multispectral Remote Sensing Image Processing

Fast and Accurate Hyperspectral Image Classification with Window Shape Adaptive Singular Spectrum Analysis

Hyperspectral classification is a task of significant importance in the field of remote sensing image processing, with attaining high precision and rapid classification increasingly becoming a research focus. The classification accuracy depends on the degree of raw HSI feature extraction, and the use of endless classification methods has led to an increase in computational complexity. To achieve high accuracy and fast classification, this study analyzes the inherent features of HSI and proposes a novel spectral–spatial feature extraction method called window shape adaptive singular spectrum analysis (WSA-SSA) to reduce the computational complexity of feature extraction. This method combines similar pixels in the neighborhood to reconstruct every pixel in the window, and the main steps are as follows: rearranging the spectral vectors in the irregularly shaped region, constructing an extended trajectory matrix, and extracting the local spatial and spectral information while removing the noise. The results indicate that, given the small sample sizes in the Indian Pines dataset, the Pavia University dataset, and the Salinas dataset, the proposed algorithm achieves classification accuracies of 97.56%, 98.34%, and 99.77%, respectively. The classification speed is more than ten times better than that of other methods, and a classification time of only about 1–2 s is needed.

Retracted: Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software

Retracted: Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software