Review
Computer Science, Artificial Intelligence
Sin Liang Lim, Jaya Sreevalsan-Nair, B. S. Daya Sagar
Summary: This article provides a brief overview of various aspects of data mining of multi spectral image data, with a focus on remote sensing satellite images acquired using multispectral imaging. It reviews different data mining processes, state-of-the-art methods, and applications. The article also emphasizes the importance of understanding data acquisition and preprocessing, and concludes with applications demonstrating knowledge discovery, challenges, and future directions for MSI data mining research.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2023)
Article
Agronomy
Haolu Li, Guojie Wang, Zhen Dong, Xikun Wei, Mengjuan Wu, Huihui Song, Solomon Obiri Yeboah Amankwah
Summary: This study utilized deep learning technology to achieve the identification of cotton crop fields in Wei-Ku region, China, using high-resolution remote sensing images, with optimized training of the model. Results showed significant improvements in performance and accuracy with the DenseNet model.
Article
Environmental Sciences
Wanrou Qin, Yan Song, Haitian Zhu, Xinli Yu, Yuhong Tu
Summary: This article utilizes satellite remote sensing data for dynamic monitoring of shipyard production state and proposes an improved evidence fusion method to solve the conflict of evidence, thereby improving monitoring accuracy.
Article
Environmental Sciences
Tianhao Mu, Guiwei Liu, Xiguang Yang, Ying Yu
Summary: Soil moisture is an important component of soil parameterization and plays a significant role in the global hydrological cycle. Remote sensing is a crucial method for estimating soil moisture, and this study developed a new nonlinear Erf-BP neural network method using integrated multiple-resource remote sensing data to establish a soil moisture estimation model. The results showed that using multiple-resource remote sensing data provided better accuracy for the soil moisture estimation model. Moreover, the SMC predicted results using the new Erf-BP neural network with multiple-resource remote sensing data had a good overall correlation coefficient of 0.6838 and improved accuracy compared to the linear model.
Article
Environmental Sciences
Zhihui Li, Jiaxin Liu, Yang Yang, Jing Zhang
Summary: A method for disparity refinement based on plane segmentation was proposed in this study, which accurately fits planes in the presence of noise and approximates surfaces through plane combination.
Article
Remote Sensing
Lijing Han, Jianli Ding, Xiangyu Ge, Baozhong He, Jinjie Wang, Boqiang Xie, Zipeng Zhang
Summary: This study evaluates the applicability of four commonly used spatiotemporal fusion algorithms for monitoring soil salinity and assesses soil salinity using a random forest regression model. The results show that the spatiotemporal fusion algorithm can generate accurate fused images, which is crucial for monitoring soil salinity.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Engineering, Civil
Wenbin Zhu, Shengrong Tian, Jiaxing Wei, Shaofeng Jia, Zikun Song
Summary: Advances in satellite remote sensing techniques have led to the development of global evapotranspiration models and the production of freely available ET products. This study evaluated the accuracy and uncertainty of five RS-based global ET products at multiple scales, showing variations in accuracy and uncertainty depending on the product and scale.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Geological
Libo Cheng, Jia Li, Ping Duan, Mingguo Wang
Summary: The study focuses on improving the speed, accuracy, and parameters of landslide detection models through introducing an attention mechanism to enhance model accuracy. The effectiveness of the YOLO-SA model for potential landslide detection is verified with an F1 score of 90.65%, showing reduced parameters, improved accuracy, and faster speed compared to other advanced models.
Review
Ecology
Jinna Lv, Qi Shen, Mingzheng Lv, Yiran Li, Lei Shi, Peiying Zhang
Summary: Semantic segmentation is a challenging task in pixel-level remote sensing data analysis. Deep learning methods have been successfully applied and improved in this field, leading to excellent results. However, there is still a deficiency in the evaluation and advancement of semantic segmentation techniques for remote sensing data. This paper surveys more than 100 papers in the past 5 years and comprehensively summarizes the advantages and disadvantages of techniques and models based on important and difficult points, providing valuable insights for beginners in this field.
FRONTIERS IN ECOLOGY AND EVOLUTION
(2023)
Article
Environmental Sciences
Claudia Maria Nascimento, Wanderson de Sousa Mendes, Nelida Elizabet Quinonez Silvero, Raill Roberto Poppiel, Veridiana Maria Sayao, Andre Carnieletto Dotto, Natasha Valadares dos Santos, Merilyn Taynara Accorsi Amorim, Jose A. M. Dematte
Summary: Research on soil degradation is crucial for environmental protection and land management. By combining satellite images with environmental information, a Soil Degradation Index (SDI) was developed to classify soil degradation levels into five groups, supporting decision-making on land use planning and management. Through comprehensive analysis of various factors, the study contributes significantly to understanding and addressing soil degradation issues.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Geosciences, Multidisciplinary
Xianglin He, Lin Yang, Anqi Li, Lei Zhang, Feixue Shen, Yanyan Cai, Chenhu Zhou
Summary: This study verified the effectiveness of phenological parameters and remote sensing predictors extracted from the Sentinel-2 EVI time series for DSM in farmland.
Article
Chemistry, Analytical
Adekanmi Adeyinka Adegun, Jean Vincent Fonou Dombeu, Serestina Viriri, John Odindi
Summary: Object detection in high-resolution remote sensing satellite images is critical for various purposes, including disaster prevention, service delivery, and urban/rural planning. This study evaluated the performance of deep learning-based object detection methods on a new dataset of diverse features. The results showed that YOLOv8 achieved the highest detection accuracy of more than 90% and the fastest detection speed of 0.2 ms.
Article
Environmental Sciences
Svetlana Illarionova, Sergey Nesteruk, Dmitrii Shadrin, Vladimir Ignatiev, Maria Pukalchik, Ivan Oseledets
Summary: The paper introduces a novel image augmentation approach named MixChannel, which improves the accuracy and performance of solving segmentation and classification tasks with multispectral satellite images by mixing auxiliary data from different locations.
Article
Environmental Sciences
James Kobina Mensah Biney, Mohammadmehdi Saberioon, Lubos Boruvka, Jakub Houska, Radim Vasat, Prince Chapman Agyeman, Joao Augusto Coblinski, Ales Klement
Summary: Soil organic carbon (SOC) plays a crucial role in soil quality, food security, and climate change mitigation. Spectroscopy under proximal sensing is effective in predicting SOC, but limitations exist in estimating it on a larger spatial scale. This study compared the capabilities of small Unmanned Aircraft Systems (UAS), Sentinel-2, and field spectroscopy in monitoring and predicting SOC content, with UAS showing better accuracy in modeling using random forest (RF) and field spectroscopy demonstrating better overall results using support vector machine regression (SVMR).
Article
Computer Science, Information Systems
Hui Lu, Qi Liu, Xiaodong Liu, Yonghong Zhang
Summary: This paper introduces and analyzes the research and application progress of remote sensing image satellite data processing from the perspective of semantics, with a focus on technical advancements in the field of semantic construction, particularly deep learning technology. Furthermore, it discusses in detail the challenges and problems in semantic description, semantic classification, and semantic search, aiming to provide more directions for future exploration.
JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
(2021)
Article
Environmental Sciences
Jerzy Cierniewski, Jean-Louis Roujean, Jaroslaw Jasiewicz, Slawomir Krolewicz
Summary: Tillage can decrease the amount of net shortwave radiation absorbed by air-dried bare arable land in cloudy-sky conditions. The study assessed the variations in surface albedo by combining different observations and model equations.
Article
Spectroscopy
Krzysztof Dyba, Roman Wasala, Jan Piekarczyk, Elzbieta Gabala, Magdalena Gawlak, Jaroslaw Jasiewicz, Henryk Ratajkiewicz
Summary: We used noninvasive point reflectance spectroscopy and machine learning to study scales on the brown and golden iridescent areas on the dorsal side of the forewing of Diachrysia chrysitis and D. stenochrysis. We were able to distinguish between these moth species and validate our approach using a statistically significant collection of specimens.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Article
Meteorology & Atmospheric Sciences
Karolina Herodowicz-Mleczak, Jan Piekarczyk, Cezary Kazmierowski, Jakub Nowosad, Mateusz Mleczak
Summary: The aim of this study was to build predictive models for soil roughness based on different tillage tools, roughness indices, and soil properties. The results showed that a single index is not sufficient to accurately describe post-treatment soil roughness. Linear and random forest models were built to analyze the relationships between roughness indices, tillage tools, and soil properties. The models demonstrated the importance of considering both the macro and micro scale roughness indices. These predictive models can be effectively applied to estimate various soil properties.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Environmental Sciences
Adam Mlynarczyk, Slawomir Krolewicz, Monika Konatowska, Grzegorz Jankowiak
Summary: This study focuses on the experiments using the Yuneec E10T thermal imaging camera in environmental research and evaluates the camera's radiometric calibration and stability.
Article
Environmental Sciences
Krzysztof Dyba, Sofia Ermida, Mariusz Ptak, Jan Piekarczyk, Mariusz Sojka
Summary: Changes in lake water temperature may significantly affect the functioning of these ecosystems. This study developed a remote sensing method using Landsat 8 imagery to monitor lake water temperature. Two models, linear regression and random forest, were developed to estimate surface water temperature. The results showed that the random forest method provided the most accurate estimation.
Article
Environmental Sciences
Adam Mlynarczyk, Monika Konatowska, Slawomir Krolewicz, Pawel Rutkowski, Jan Piekarczyk, Wojciech Kowalewski
Summary: This study explores the relationship between water content in forest habitats and spectral indices using remote sensing methods. The normalized difference vegetation index (NDVI) was found to be the most useful in assessing moisture variation. The study also highlights the impact of water reservoirs on habitat humidity and trophicity.
Article
Agronomy
Jan Piekarczyk, Andrzej Wojtowicz, Marek Wojtowicz, Jaroslaw Jasiewicz, Katarzyna Sadowska, Natalia Lukaszewska-Skrzypniak, Ilona Swierczynska, Katarzyna Pieczul
Summary: In this study, three fungi species were discriminated using hyperspectral and RGB data and machine learning methods. The research showed the possibility of distinguishing the fungi species based on hyperspectral curves and RGB images. The wavelengths in the SWIR region were effective in distinguishing certain fungi species, while the visible range of the electromagnetic spectrum was effective in distinguishing others.
Article
Astronomy & Astrophysics
Karolina Herodowicz-Mleczak, Jan Piekarczyk, Henryk Ratajkiewicz, Jakub Nowosad, Szymon Sledz, Cezary Kazmierowski, Slawomir Krolewicz, Roman Kierzek
Summary: The purpose of this study was to parameterize soil surface roughness (SSR) based on proximal measurements of spectral reflectance in the VIS-NIR range, which is important for monitoring the state of soil surfaces. The study found that there is a relationship between SSR parameters and soil spectra, and different wavelengths are best predictors depending on the spectral transformation method.
EARTH AND SPACE SCIENCE
(2023)
Article
Geography
Agnieszka Glinko, Cezary Kazmierowski, Jan Piekarczyk, Slawomir Krolewicz
Summary: This study assessed the suitability of multispectral data acquired with an agricultural digital camera in determining soil properties. The results showed that the camera is suitable for assessing soil properties such as granulometric composition, pH, and element content. The Cubist regression model performed better than the partial least squares model.
QUAESTIONES GEOGRAPHICAE
(2022)
Article
Geosciences, Multidisciplinary
Lidia Zuk, Slawomir Krolewicz
Summary: This article demonstrates the use of Sentinel images for heritage protection and management in rural landscapes undergoing dynamic transformations. Construction works and modern farming practices pose challenges to the preservation of archaeological structures. Through the analysis of radar and optical data from Sentinel-1 and -2, trends in building development and land use changes can be identified.
Article
Environmental Studies
Elton Mammadov, Michael Denk, Frank Riedel, Cezary Kazmierowski, Karolina Lewinska, Remigiusz Lukowiak, Witold Grzebisz, Amrakh Mamedov, Cornelia Glaesser
Summary: Soil spectroscopy combined with partial least squares regression can effectively predict soil elements and basic properties, with higher prediction accuracy using first derivative spectra. The performance of prediction models varies for different soil properties, with the major prediction mechanisms involving the correlations of elements with CaCO3, pH, clay content, and mineralogy.
