Remote Sensing Satellite Images Research Articles

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

Evaluating Shoreline Changes at Ayvacık Reservoir (Çanakkale, Türkiye) Through Remote Sensing and Geographic Information System Techniques: A Twelve-Year Assessment

This study aims to determine the spatial and temporal changes occurring along the shoreline of the Ayvacık Reservoir in Çanakkale, Türkiye. Landsat 8 OLI/TIRS and Landsat 7 ETM+ satellite images were analyzed using remote sensing and geographic information system techniques. The dataset used in the study covers the period between the completion of the dam construction in 2008 and 2019. Preprocessing of the remote sensing satellite images and digital image processing analyses were carried out using ENVI and ArcGIS software. The shoreline was determined through manual digitization. Consequently, it was found that the shoreline length was 14.994 km in 2008 and increased to 22.293 km in 2019. These values represent the observed minimum and maximum shoreline lengths, respectively. The study period revealed an increase in shoreline length. Given that this study is the first to elucidate shoreline changes occurring at the Ayvacık Reservoir, it is anticipated to provide essential insights for water resource managers by contributing significantly to the literature.

Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost

Chlorophyll-a (Chl-a) concentration is one of the important indicators in water bodies for assessing the ecological health of water quality. In this paper, an OGolden-DBO-XGBoost Chl-a concentration inversion model is proposed using Wuliangsu Lake as the study area, and by combining the Sentinel-2 remote-sensing satellite images and measured Chl-a concentration data in Wuliangsu Lake, the XGBoost model is optimized using the hybrid-strategy-improved dung beetle optimization algorithm (OGolden-DBO), and an OGolden-DBO-XGBoost Chl-a concentration inversion model. The OGolden-DBO-XGBoost model’s coefficients of determination (R2s) were 0.8936 and 0.8850 on the training set and test set, according to the results. The root mean squared errors (RMSEs) were 3.1353 and 2.9659 μg/L, and the mean absolute errors (MAEs) were 1.8918 and 2.4282 μg/L. The model performed well and provided a strong support for the detection of Chl-a concentration in Wuliangsu Lake.

ESatSR: Enhancing Super-Resolution for Satellite Remote Sensing Images with State Space Model and Spatial Context

Super-resolution (SR) for satellite remote sensing images has been recognized as crucial and has found widespread applications across various scenarios. Previous SR methods were usually built upon Convolutional Neural Networks and Transformers, which suffer from either limited receptive fields or a lack of prior assumptions. To address these issues, we propose ESatSR, a novel SR method based on state space models. We utilize the 2D Selective Scan to obtain an enhanced capability in modeling long-range dependencies, which contributes to a wide receptive field. A Spatial Context Interaction Module (SCIM) and an Enhanced Image Reconstruction Module (EIRM) are introduced to combine image-related prior knowledge into our model, therefore guiding the process of feature extraction and reconstruction. Tailored for remote sensing images, the interaction of multi-scale spatial context and image features is leveraged to enhance the network’s capability in capturing features of small targets. Comprehensive experiments show that ESatSR demonstrates state-of-the-art performance on both OLI2MSI and RSSCN7 datasets, with the highest PSNRs of 42.11 dB and 31.42 dB, respectively. Extensive ablation studies illustrate the effectiveness of our module design.

Multi-Sensor Satellite Remote-Sensing Data for Exploring Carbonate-Hosted Pb-Zn Mineralization: Akhlamad Area, Razavi Khorasan, North East Iran

The exploration of Pb-Zn mineralization in carbonate complexes during field campaign is a challenging process that consumes high expenses and time to discover high prospective zones for a detailed exploration stage. In this study, multi-sensor remote-sensing imagery from Landsat-8, Sentinel-2, and ASTER were utilized for Pb-Zn mineralization prospectivity mapping in the Akhlamad carbonate complex area, Razavi Khorasan, NE Iran. Due to the presence of carbonate formations and various evidence of Pb-Zn mineralization, this area was selected. Band composition, band ratio, principal component analysis (PCA), and SAM techniques for mapping alteration minerals as well as lineament analysis were implemented. Subsequently, a fuzzy logic model for identifying the prospective zones of Pb-Zn mineralization using multi-sensor remote-sensing satellite images was designed. The weight of each exploratory layer was determined using the fuzzy hierarchical method and the integration process of the information layers was performed using fuzzy operators. Finally, the existing mineral indications were used to evaluate and validate the obtained mineral potential map. The outcome of this investigation suggested several high-potential zones for Pb-Zn exploration in the study area.

Geological mapping and mineral exploration in Francistown region, north east Botswana from integration of ASTER TIR and aeromagnetic data

To map the region's geology and mineral exploration, this study looked at how remote sensing satellite imaging from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Thermal Infrared (TIR) sensor and aircraft geophysics may be used. The Francistown region forms part of the Zimbabwe craton, northeast of Botswana. It comprises felsic rock types, including granite and dry river sand, mafic-ultramafic rocks, and gneiss country rock. Based on their spectral emissivity responses, mafic-ultramafic units and quartz-rich units were discovered. These units are important targets for the discovery of mineral resources. The research region can be described as convoluted, fractured, intruded, metamorphosed, and ultimately worn from a geological and geophysical standpoint. Several remote sensing and aeromagnetic approaches were employed. The false color composite (FCC) and mineral indices using ASTER TIR data provided helpful information for delineating the area's geology and concentration of major-rock-forming minerals. The aeromagnetic data extracted lineaments well, and the Center for Exploration Target (CET) grid analysis system was very helpful. The CET Porphyry Analysis technique was applied in mapping near-circular porphyry features, which are good indicators of possible sites with high mineralization potential. The analysis of the lineament map shows that mineral deposits are mostly along the NE-SW direction. The results from the ASTER TIR and aeromagnetic enhancements indicate the occurrence of mafic-ultramafic rocks and felsic-rich units, which have been documented to host mineralization in the region. Porphyry structures are also mapped successfully along the general strike of the geological structure in the study area. The combination of these methods proved their success, and they can be used further in other regions of high mineral potential and lithological mapping for their cost efficiency and reliability.

Remote Sensing Image Segmentation for Aircraft Recognition Using U-Net as Deep Learning Architecture

Recognizing aircraft automatically by using satellite images has different applications in both the civil and military sectors. However, due to the complexity and variety of the foreground and background of the analyzed images, it remains challenging to obtain a suitable representation of aircraft for identification. Many studies and solutions have been presented in the literature, but only a few studies have suggested handling the issue using semantic image segmentation techniques due to the lack of publicly labeled datasets. With the advancement of CNNs, researchers have presented some CNN architectures, such as U-Net, which has the ability to obtain very good performance using a small training dataset. The U-Net architecture has received much attention for segmenting 2D and 3D biomedical images and has been recognized to be highly successful for pixel-wise satellite image classification. In this paper, we propose a binary image segmentation model to recognize aircraft by exploiting and adopting the U-Net architecture for remote sensing satellite images. The proposed model does not require a significant amount of labeled data and alleviates the need for manual aircraft feature extraction. The public dense labeling remote sensing dataset is used to perform the experiments and measure the robustness and performance of the proposed model. The mean IoU and pixel accuracy are adopted as metrics to assess the obtained results. The results in the testing dataset indicate that the proposed model can achieve a 95.08% mean IoU and a pixel accuracy of 98.24%.

Identification of urban green space availability and utilization in Semarang City

Background: Urban green space land a has been used differently because of Semarang City's rapid development in several sectors, including industry, transportation, and economics. Gajahmungkur Subdistrict determined that the minimum urban green space requirement of 30% had been reached. Methods: The method used in this research is quantitative method with remote sensing. Quantitative methods are used as systematic, rational, measurable, objective, and concrete methods. Findings: However, the identification results may not represent the total amount of urban green space in the village. Conclusion: Therefore, using remote sensing satellite image interpretation of Sampangan Village, the results of urban green space identification amounted to 22.97 ha or 22.6% of the 77.4% non-urban green space area with most of the land being urban green space of riparian type connected to Sampangan Park as a form of utilizing riparian urban green space which is frequently used by visitors to traders. In addition, Kelurahan Sampangan has urban green space utilization in the form of river borders, Sampangan Park, Sampangan Mitra Field, UNNES Labschool Elementary School sports field, Wotgaleh Cemetery, and parking lots. Sampangan Village only has 22.6% of the required minimum of 30% of urban green space, so it still falls short of the standard. Thus, efforts are needed to increase the provision of urban green space in Sampangan Village to become a residential area that is suitable for people to live in from an environmental aspect.

Spatial–Temporal Evolution Analysis of the Development of Jingdang and Famen Townships in the Vicinity of the Capital City Site of the Western Zhou Dynasty in China

This study aimed to explore the evolution of Jingdang and Famen towns near the Zhouyuan site, the capital city site of the Western Zhou Dynasty in China, to elucidate the relationship between township development and the essential ancient relics conservation areas. Based on the remote sensing satellite images from 1982 to 2022, combining with historical demographic data, this study used four methods, including land use classification, land use transfer matrix, landscape pattern center of gravity changes, and population count statistics, to study the spatial–temporal evolution of land, population, and ecology in the region over the past 40 years. The results showed that under the strict relic conservation regimen, these two towns are in a declining stage. To improve their decaying status, some potential strategies valuable for township development are proposed to balance the relationship between relic conservation and township development for their mutual benefit and coexistence.

Hybridized Clustering Algorithm and Ensemble Learning for Monitoring Paddy Crop Growth Analysis

In food security control and agricultural management, crop monitoring is important to improve the crop yield rate and ensure the paddy growth. The paddy crop is monitored via remote sensing satellite images that cover the entire cultivating region. The satellite images are affected due to satellite movement and human errors, which causes over fitting issues. In addition, the remote images are facing difficulties while managing system robustness. The research issue is overcome by applying the Hybridized Clustering Algorithm-based Ensemble Learning Hybridized Clustering Algorithm with Ensemble Learning (HCA-EL) approach. Initially, the satellite images are handled to remove geometric, radiometric, and atmospheric corrections using pre-processing techniques. This process uses the Ground Control Points (GCP) and histogram process to remove unwanted information. Then vegetation index value data points are obtained to progress the overall crop monitoring. The data points are analyzed using spectral clustering based on the eigenvector and eigenvalue, minimizing the dimensionality issues. Finally, the crop monitoring criteria, such as soil quality and water levels are computed to improve paddy growth. The monitoring process is done with the help of the Ensemble Learning Extreme Networks (ELEN), which uses the voting criteria to identify the quality parameters. The HCA-EL approach-based crop monitoring process was implemented using the Python tool with respective performance metrics like Accuracy, Error Rate, MCC and F1-Score.

Remote sensing inversion study of total organic carbon concentration in Karst Plateau Lakes–Taking Pingzhai reservoir as an example

Currently, the inversion of remote sensing satellite images of water environment indicators mostly stays in the indicators with active optical characteristics, while there is less research on the inversion of most water quality indicators with non-optical activity properties, weak scattering and absorption of optical radiation, the size of their concentration has little effect on the spectral characteristics of the water body, such as TOC(Total Organic Carbon). In this paper, based on Pingzhai Reservoir, a dammed river in the karst mountainous area, the inversion model of TOC concentration was established based on BP neural network (BPNN) and sentinel-2 satellite remote sensing images. The results showed that the single bands with high correlation with the measured TOC concentration data were two vegetation red-edge bands B6 (740 nm) and B7 (783 nm) and one NIR band B8 (842 nm), and finally b7, b6 + b7, b7 + b8, b7 × b8 were selected as the input layers of BPNN for modeling through the combination of the bands, and their Pearson’s coefficients were −0.667, −0.656, −0.655, − 0.675.The inverse model established could reach a minimum RMSE of 0.235 mg/L and a maximum R 2 of 0.889, which was superior to that of the conventional empirical model. Demonstrate the feasibility of a TOC inversion method based on Sentinel-2 data and BPNN to monitor TOC concentrations in Pingzhai Reservoir. The study successfully established a BP neural network inversion model of TOC concentration in Pingzhai Reservoir with low error, meanwhile, we analyzed the correlation between common water quality indicators and TOC in the reservoir, in which TOC showed significant positive correlation with WT and significant negative correlation with TN and EC, with Pearson’s coefficients of 0.655, −0.666, and −0.393, respectively. The article provides scientific theoretical foundation and technical support for water quality protection of water sources.

Performance evaluation of Machine Learning algorithms for LULC classification: A case study of Fez-Meknes region

Significant advancements have been made in remote sensing technologies, with increasingly refined applications in creating LULC maps. The integration of Machine Learning-based approaches has been explored to develop LULC maps with varying levels of precision, leveraging diverse satellite imagery. However, the task of producing LULC maps for extensive areas like the Fez-Meknes region, covering an area of approximately 40,075 km2, can be challenging using traditional methodologies. Thus, this study prioritized the major objective of establishing a reference for extracting LULC information. This endeavour involves the comparative assessment of the performance of different LULC classification approaches: Recursive Partitioning (Rpart), k-nearest neighbors (KNN), random forest (RF), Linear Discriminant Analysis (LDA), support vector machine (SVM), and extreme gradient boosting (XGBoost). For map production, remote sensing data and a supervised classification algorithm based on LANDSAT images of the Fez-Meknes region were employed. The accuracy of the generated maps was assessed using overall accuracy and Kappa coefficient. This methodology holds the potential to be replicated in other regions, utilizing a variety of available remote sensing satellite images to generate LULC maps. Essentially, the approach proposed in this study will be a valuable tool for planners, facilitating the acquisition of LULC maps at various time intervals. This will facilitate the classification of land cover types in a faster and more cost-effective manner.

Advancing sun glint correction in high-resolution marine UAV RGB imagery for coral reef monitoring

Sun glint presents a significant challenge in marine ecological remote sensing by obscuring valuable features of benthic communities, thus hindering accurate monitoring of these communities. More specifically, sun glint leads to inaccurate coral reef identification when it comes to utilize the high-resolution marine Unmanned Aerial Vehicle (UAV) imagery. Despite the availability of many physical models that address this problem using ocean remote sensing satellite images, there is still a lack of effective sun glint correction solutions for high-resolution RGB imagery obtained by consumer-grade UAVs in marine ecological monitoring, particularly at the centimeter level. In this study, an innovative sun glint correction pipeline combining a Foreground Attention-based Semantic Segmentation Network (FANet) with optical flow-based pixel propagation has been proposed, aiming to achieve accurate sun glint detection and restoration of real texture of coral reefs. To further understand the inner mode of sun glint segmentation FANet model, we conduct explainable analysis to ensure that it learns the informative sun glint features and makes reliable decisions. According to the comprehensive experimental evaluation using three datasets, our FANet model exhibits accurate and effective sun glint detection, achieving an IoU range of 70.86%–83.96%. Moreover, the sun-glint free images processed by our sun glint correction method have been employed for large-scale image stitching and shallow-water coral reef habitat semantic mapping. The results demonstrate that our sun glint correction approach successfully restores the texture of coral reefs and prepares a reliable foundation for subsequent downstream applications. We believe that our method as an open-source sun glint correction solution in high-resolution marine UAV RGB images is able to enhance the accuracy and efficiency of various marine ecological remote sensing tasks at a low-cost. The relevant code can be found in https://github.com/jyqinnn/Sun-glint-correction.

LSKF-YOLO: Large Selective Kernel Feature Fusion Network for Power Tower Detection in High-Resolution Satellite Remote Sensing Images

LSKF-YOLO: Large Selective Kernel Feature Fusion Network for Power Tower Detection in High-Resolution Satellite Remote Sensing Images

A Unified Super-Resolution Framework of Remote-Sensing Satellite Images Classification Based on Information Fusion of Novel Deep Convolutional Neural Network Architectures

A Unified Super-Resolution Framework of Remote-Sensing Satellite Images Classification Based on Information Fusion of Novel Deep Convolutional Neural Network Architectures

Restoration of remote satellite sensing images using machine and deep learning: A survey

Remote sensing satellite images are affected by different types of degradation, which poses an obstacle for remote sensing researchers to ensure a continuous and trouble-free observation of our space. This degradation can reduce the quality of information and its effect on the reliability of remote sensing research. To overcome this phenomenon, the methods of detecting and eliminating this degradation are used, which are the subject of our study. The original aim of this paper is that it proposes a state of art of recent decade (2012-2022) on advances in remote sensing image restoration using machine and deep learning, identified by this survey, including the databases used, the different categories of degradation, as well as the corresponding methods. Machine learning and deep learning based strategies for remote sensing satellite image restoration are recommended to achieve satisfactory improvements.

Multispectral data mining: A focus on remote sensing satellite images

AbstractThis article gives a brief overview of various aspects of data mining of multispectral image data. We focus on specifically the remote sensing satellite images acquired using multispectral imaging (MSI), given the technology used across multiple knowledge domains, such as chemistry, medical imaging, remote sensing, and so on with a sufficient amount of variation. In this article, the different data mining processes are reviewed along with state‐of‐the‐art methods and applications. To study data mining, it is important to know how the data are acquired and preprocessed. Hence, those topics are briefly covered in the article. The article concludes with applications demonstrating the knowledge discovery from data mining, modern challenges, and promising future directions for MSI data mining research.This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Big Data Mining

Using Medium-Resolution Remote Sensing Satellite Images to Evaluate Recent Changes and Future Development Trends of Mangrove Forests on Hainan Island, China

Mangroves are endemic forest communities in tropical and subtropical coastal zones. China’s Hainan Island is very rich in mangrove plant species. In recent years, due to climate change and human disturbance, the living environment of many mangrove distribution areas has been seriously threatened. This study used land satellite series remote sensing images from 1990 to 2020 to monitor the coverage and area changes of mangroves on Hainan Island. The spatial distribution pattern and change trend of mangroves were explored using the standard deviation ellipse method, and the CA–Markov model was used to predict the possible changes of mangroves in the study area in 2025 and 2030. The development trend of mangroves in the future and the driving factors affecting the evolution of mangroves were also analyzed. Over the past 30 years, the area of mangroves has witnessed fluctuations. It decreased from 4578 hectares in 1990 to its lowest at 3870 hectares in 2005 and then rebounded to 4474 hectares by 2020, indicating an initial decline followed by a gradual increase. Although conservation and restoration efforts have yielded success, specific areas have witnessed a decline in mangrove coverage. From 1990 to 2020, mangrove areas in Huiwen decreased from 1055 hectares to 904 hectares, areas in Guannan decreased from 227 hectares to 167 hectares, areas in Xinyinggang decreased from 328 hectares to 298 hectares, areas in Yangpugang decreased from 747 hectares to 682 hectares, areas in Huachangwan decreased from 355 hectares to 327 hectares, and areas in Puqian decreased from 170 hectares to 141 hectares. In particular, the growth in the Eastern and Mayao port areas is especially significant. Additionally, data analysis has revealed the spatial distribution characteristics of mangroves in different regions, such as the mangrove area in Dongzhaigang, which remained relatively stable from 1990 to 2020, while in other areas like Huiwen and Guannan, the mangrove area decreased during these 20 years. By calculating the standard deviation ellipse, we observed that the overall change of mangroves on Hainan Island from 1990 to 2020 was relatively slow, mainly distributed along the northern coastal area of Hainan Island. Furthermore, the standard deviation ellipse and SDE center point of each mangrove growth area have visualized the growth trends of the mangroves. The Markov chain simulation results show that future changes in mangroves will mainly be concentrated in the marginal areas of the mangroves. These areas may be affected by rising sea levels, climate change, soil salinity, and human activities. In the future, mangrove areas are expected to display a dynamic equilibrium, experiencing periods of expansion and reduction, ultimately moving towards a more consistent state. To protect and restore mangroves, it is necessary to strengthen the monitoring and management of their ecological environment and socio-economic factors and improve their stability and diversity.

SAR U-Net: Spatial attention residual U-Net structure for water body segmentation from remote sensing satellite images

SAR U-Net: Spatial attention residual U-Net structure for water body segmentation from remote sensing satellite images

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring.