Satellite Images Research Articles

Comparative study on satellite based image encryption methods: a survey

The availability of high-resolution satellite images increases with advancements in remote sensing technology. These satellite images are used in various earth observation applications such as disaster management, military applications, weather forecasting, land use and cover, and many more. Satellite images have large volumes stored in memory devices. These satellite images are transmitted to the ground station for processing and analysis. In these cases, images are vulnerable to privacy issues. As technology advances, onboard processing of satellite images using intelligent systems processes the images faster. A model such as field programmable gate arrays (FPGA) is used in onboard processing to process satellite images. However, images are susceptible to faults induced by harsh radiation environments in space. Encryption is one of the most assured methods to provide privacy to satellite images. Hence, encryption of satellite images during processing, storage, and transmission is the present rising demand. There are various encryption methods implemented using algorithms such as advanced encryption standard (AES), homomorphic, advanced encryption standard-counter (AES-CTR), and chaotic maps. Concurrent processing and encryption of images using MapReduce with Hadoop Framework perform the task faster. The focus of this paper is a comparative study of the various encryption methods used in recent years.

Holdout based blending approaches for improved satellite image classification

An essential component of remote sensing, image analysis, and pattern recognition is image categorization. The classification of land use using remotely sensed data creates a map-like representation as the final form of the investigation. With its ability to effectively categorize satellite images, machine learning (ML) algorithms have gained significant traction in a number of fields, including land-use planning, disaster response, and natural resource management. Ensemble learning is also a widely used technique for enhancing the precision of satellite image categorization, which combines multiple models to get more precise predictions. Holdout is an ensemble technique, where multiple ML algorithms are used for training on the same dataset. The primary goal of this study is to create a holdout model for classifying satellite images. Initially, this study explores the usage of ML algorithms namely support vector machines (SVM), k-nearest neighbor (KNN), decision trees (DT), gradient boosting classifier (GBC), histogram-based GBC (HGBC), random forest classifier (RF), bagging classifier (BC), XGBoost classifier for classifying satellite images. Later, GBC, HGBC, RF, BC, and XGBoost are combined to build a stacking model. The bagging ensemble model outperforms all other methods and reaches an accuracy of 88.90%. Finally, blending models with holdout approach were developed and achieved accuracy of 93.70%, 94.14%, and 93.87% which outperformed all previous algorithms.

Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery

<p>The aim of this research is to compile a tsunami wave hazard scale based on built-up land density extracted and classified by machine learning from Sentinel 2A satellite and digital elevation model (DEM) imageries. This research was carried out in 5 stages, namely: (i) pre-processing of Sentinel 2A and DEM images, (ii) Classification of VI data using the machine learning algorithms, (iii) Spatial prediction using the ordinary kriging method, (iv) Field testing using the confusion matrix method, (v) Preparation of decision matrix for tsunami wave hazard. The results of the study show that the most accurate classification algorithm for classifying built-up indices data is the k-nearest neighbor (k-NN) algorithm. The results of the statistical accuracy test show that the most accurate is normalized difference built-up index (NDBI) with a mean of square error (MSE) value of 0.073 and a mean of absolute error (MAE) of 0.003. DEM analysis shows that the research area is at an altitude of 0–15 meters above sea level so it is in the high vulnerability to medium vulnerability category. Field testing showed user accuracy of 91.11%, manufacturer accuracy of 92.16%, and overall average accuracy of 91%.</p>

Advanced utilization of satellite and governmental data for determining the coverage and condition of green areas in Poland: An experimental statistics supporting the Statistics Poland

Statistics Poland utilizes Earth Observation and in-situ data to assess green areas, aiming to derive vital information on natural capital statistics and human well-being. This is essential for informed decision-making and sustainable development. Improving regional well-being statistics is a priority due to Poland's low rankings in environmental well-being indicators. To overcome challenges in obtaining comprehensive data at the commune level, the study introduces a new and innovative system for monitoring green areas. This system employs Sentinel-2 satellite data, combined with Copernicus vegetation and phenology products in order to determine extent and condition of green areas. In addition, the applied land cover product (Dynamic World) and national vector database of topographic features (BDOT10k) contributes to the quality and accuracy of the analyses carried out. The system yields two pivotal indicators: one delineating the proportional coverage of green areas within communes boundaries, and the other elucidating the state and health of these delineated areas. A key highlight of the system is its innovative use of local resources and the Google Earth Engine application to observe green areas at a 10-meter resolution at the commune level, particularly in major cities in Poland. These observations are crucial for the Polish Public Services Monitoring System (SMUP), providing comprehensive local-level evaluation services. The research outcome consists of a comparison between the extent of green areas delineated by public administration at the commune level and the extent of green areas determined using a newly proposed method, which relies on satellite imagery from the Copernicus program. The results indicate variability between these two datasets, depending on different forms of land cover within each administrative unit. The most consistent data is derived from large urban agglomerations in Poland.

Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods

Groundwater (GW) plays a crucial role in coastal aquifers and arid regions, serving as a lifeline for communities by providing a reliable and resilient water source, making its monitoring essential for sustainable water management. This study aimed at modeling GW via regionalization of the Gravity Recovery and Climate Experiment (GRACE) data based on two methods. The first method directly regionalized the GRACE data for modeling GW via in situ measurements, including the lake level, precipitation, temperature, observed GW and Penman-Monteith-Leuning (PML) evapotranspiration data. The second method included two stages, in the first stage, the GRACE data were downscaled via the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) data which contains satellite based precipitation, temperature, soil moisture, and snow water equivalent data. In the second stage, the downscaled GRACE was bias corrected to provide regionalized data. Artificial intelligence models consist of shallow networks (Feed Forward Neural Network (FFNN), Adaptive neuro fuzzy (ANFIS), Support Vector Machine (SVR)), the ensemble of shallow networks and Long-Short Term Memory (LSTM) deep learning method were employed in the modeling process and the observed GW level data were targeted for the regionalization. The Link CluE clustering ensemble method was implemented to cluster the piezometers of the aquifer to separate different GW patterns in the area. The proposed methodology was examined over the Miandoab plain, one of the sub-basins of the Lake Urmia, located in Northwest Iran. The modeling results demonstrated that the first method could exhibit superior performance with the Nash-Sutcliffe Efficiency (NSE) of up to 17% higher than the second method. Thus, using in situ observed data for downscaling proved to be more accurate than relying on the data based on the satellite imagery. The results indicated that the ensemble of shallow networks could lead to more precise results than using the deep and shallow learning models, individually, where the NSE for the ensemble of shallow networks was up to 50% higher compared to the LSTM model.

Capturing the net ecosystem CO2 exchange dynamics of tidal wetlands with high spatiotemporal resolution by integrating process-based and machine learning estimations

Accurate estimation of the net ecosystem CO2 exchange (NEE) at regional scales is of great significance for studying the carbon sink potential of coastal wetland ecosystems and their responses to global climate change. However, current NEE estimation methods are mainly developed for terrestrial ecosystems and are therefore unsuitable for NEE estimation with high spatiotemporal resolution estimation in coastal wetlands subjected to sub-daily tidal flooding. In this study, we proposed a high spatiotemporal resolution NEE estimation method for coastal marsh wetlands that properly considered tidal influence by combining the advantages of process-based modeling and machine learning. This method was verified and applied in the Changjiang estuary and Liaohe estuary marsh wetlands based on eddy covariance and environmental measurements, climate reanalysis data, and satellite images. The proposed method had good performance in the NEE estimation of tidal marsh wetlands, with Phragmites australis, Spartina alterniflora, and Suaeda salsa having coefficients of determination (R2) of 0.850, 0.676, and 0.658, respectively, and root mean square error (RMSE) values of 7.211 μmol m−2 s−1, 8.105 μmol m−2 s−1, and 0.109 μmol m−2 s−1, respectively. By integrating the tide level and salinity, the NEE estimation accuracy for each vegetation type was improved. The total annual NEE values of the Changjiang estuary and Liaohe estuary marsh wetlands in 2022 were estimated to be −0.297 and −0.444 Tg C yr−1, respectively. This study demonstrated that integrating process-based model and machine learning estimation can reliably capture the NEE dynamics of coastal wetlands, providing a useful tool to quantify coastal blue carbon potential with high spatiotemporal resolution at large scales.

Ứng dụng GIS và tư liệu viễn thám đánh giá biến động lớp phủ rừng tại vùng đệm Vườn Quốc gia Cát Tiên thuộc địa phận huyện Tân Phú, tỉnh Đồng Nai.

The Cat Tien National Park in the South of Vietnam represents a protected ecological area, it is located in tropical rainforest zone. For assessment of forest cover change in three buffer zones of Cat Tien National Park, namely Nam Cat Tien, Nui Tuong and Phu An at Tan Phu district, satellite images (Landsat 5 TM and Landsat 8 OLI) take in 1990, 2000, 2010 and 2020 were used. Land use/land cover were classified by 5 categories using maximum likelihood classifier (MLC) method, result of constructing a key for image classification showed that images without reference data for classification have over 83.50% of map accuracies. After classification, differentiation of forest land use/landcover was analysied in each year. Deforestation and agricultural area reduction took place in 1990 - 2020 period. Besides, study has quantified changes in forest cover during periods: 1990 - 2000, 2000 - 2010 and 2010 - 2020. As a result, findings show that the extents of forest cover have decreased from 5522.22 ha to 2620.98 ha during the period of 1990 - 2020. Maps of forest status as well as changes in forests can be used for forest resource management activities under the Cat Tien National Park management.

Comparative study of teachable machine for forest fire and smoke detection by drone

Forests play a vital role in maintaining ecological equilibrium and serving as vital habitats for wildlife. They regulate global climate, safeguard soil and water resources, and provide crucial ecosystem services such as air and water purification, essential for human well-being and sustainable development. Forest fires wreak havoc on ecosystems and wildlife, emitting harmful pollutants, disrupting communities, and increasing the risk of erosion and landslides. Detecting forest fires through satellite imaging, aerial reconnaissance, and ground-based sensors is pivotal for early detection and containment, safeguarding human lives, wildlife, and preserving natural resources for future generations. Utilizing drones and deep learning (DL) algorithms can significantly enhance early fire detection and minimize their devastating impact. In this paper, we examine teachable machine, a Google tool for creating DL models. We compare the top model generated by teachable machine for fire and smoke detection to models obtained through transfer learning from established DL models in image recognition and computer vision (CV), such as VGG16, VGG19, MobileNet, MobileNetv2, and MobileNetv3. The results underscore the significance of employing the teachable machine model in specific fire and smoke detection scenarios.

Incorporating Season and Solar Specificity Into Renderings Made by a NeRF Architecture Using Satellite Images.

As a result of Shadow NeRF and Sat-NeRF, it is possible to take the solar angle into account in a NeRF-based framework for rendering a scene from a novel viewpoint using satellite images for training. Our work extends those contributions and shows how one can make the renderings season-specific. Our main challenge was creating a Neural Radiance Field (NeRF) that could render seasonal features independently of viewing angle and solar angle while still being able to render shadows. We teach our network to render seasonal features by introducing one more input variable - time of the year. However, the small training datasets typical of satellite imagery can introduce ambiguities in cases where shadows are present in the same location for every image of a particular season. We add additional terms to the loss function to discourage the network from using seasonal features for accounting for shadows. We show the performance of our network on eight Areas of Interest containing images captured by the Maxar WorldView-3 satellite. This evaluation includes tests measuring the ability of our framework to accurately render novel views, generate height maps, predict shadows, and specify seasonal features independently from shadows. Our ablation studies justify the choices made for network design parameters.

Kudzu invasion and its influential factors in the southeastern United States

As an invasive plant species with wide ecological tolerance and fast infestation, kudzu has posed persistent challenges to the ecosystem and economy in the southeastern United States. However, current studies on kudzu mapping and invasive mechanisms are inadequate to unveil the characteristics of kudzu’s spatiotemporal dynamics. In this study, we investigate kudzu distribution and its influential factors based on continuous Sentinel-2 satellite images and a multi-temporal mapping strategy. The study was conducted in Knox County, Tennessee, a representative region highly impacted by kudzu in the southeastern United States. Our results revealed a steady increase in the kudzu invasion area from 2020 to 2022, with a phenology of a growth season from May to September (peak in August) and a rapid defoliation in the fall. We found precipitation is not a limiting factor for kudzu growth, while temperature has strong correlations with kudzu growth, exhibiting seasonal variations in this humid subtropical region. Human activities play an important role in kudzu distribution with a sharp decrease in kudzu density away from road networks, residential clusters, and the edges of urban forests. These findings provide valuable insights into the complex nature of the kudzu invasion in Knox County, as well as other similar areas in the southeastern United States. The observed unique growth patterns of kudzu and its interactions with various environmental factors in this study are also of importance for future initiatives related to mapping the distribution and devising strategies for the prevention and control of kudzu invasion.

Cloud Cover Segmentation and Motion Prediction from Satellite Imagery

Abstract: This research project is dedicated to achieving precise identification, segmentation, and motion prediction of cloud formations in satellite imagery. Leveraging the powerful U-Net, a renowned deep learning architecture for image segmentation, is crucial in addressing the intricate challenge of cloud detection and segmentation within remote sensing imagery. The automation of cloud identification processes within the project not only enhances weather forecasting capabilities but also contributes significantly to advancements in climate monitoring and environmental analysis. The incorporation of Long ShortTerm Memory (LSTM) networks facilitates cloud motion prediction, providing insights into the dynamic behavior of cloud formations over time. The robustness of the U-Net model, coupled with the enhanced capability of capturing intricate patterns and predicting cloud motion, establishes it as a valuable and comprehensive tool. This contributes to improving the accuracy and efficiency of cloud segmentation in satellite imagery, fostering progress in critical domains such as environmental research and meteorological applications

Detection of Water bodies using Satellite Imagery based on Deep Learning

Abstract: Satellite images are important for monitoring and managing natural resources. Water bodies such as lakes, rivers, and oceans are of great importance to the environment and human populations. The identification of water body pixels in satellite images is therefore an important task for environmental management and planning. In recent years, deep learning has shown great promise in image processing, including object detection and segmentation. This study employs CNNs and RNNs to identify water body pixels in satellite images, aiming to create a model that can accurately distinguish water and non-water pixels. This model has practical applications in environmental monitoring and management

A Comprehensive Study on Detection of Water Bodies using Satellite Imagery

Abstract: Satellite imagery plays a crucial role in overseeing and safeguarding natural resources, particularly water bodies like lakes, rivers, and oceans, which hold significant environmental and human value. Detecting water body pixels in satellite images is a key aspect of environmental management and planning. Recent advancements in deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), offer promising solutions for tasks like object detection and segmentation in image processing. This study leverages CNNs and RNNs to precisely identify water body pixels in satellite images, aiming to develop a model capable of accurately distinguishing between water and non-water pixels. Such a model holds practical significance for environmental monitoring and management efforts

Analyzing Urbanization using Satellite Image Processing

Abstract: Urbanization is directly proportional to the increase in population, which contributes to the increase in country’s GDP but affects the environment and ecosystem. This paper introduces an analysis of the satellite imagery of Mumbai for past 5 years (2019-2023). Mumbai has witnessed significant changes in the geographic region. In order to control and prevent the effects of urbanization, it is necessary to analyze the geographical changes and effects of the same. In this proposed analysis, we have analyzed urbanization with respect to changes in water, forest, urban land and barren land. Use of satellite imagery plays an important role in providing the opportunity for this analysis. Google Earth Engine (GEE) is used for the change detection for the selected area using the satellite imagery dataset provider COPERNICUS named Harmonized Sentinel-2 MSI as an input to this analysis. For this analysis we have used 3 classification methods i.e. supervised, unsurprised and NDVI

Crop Recommendation System

Abstract: In the wake of climate change and evolving agricultural practices, there is an increasing demand for innovative technologies to enhance crop productivity while ensuring sustainability. This abstract presents a comprehensive overview of a state-of-the-art Crop Recommendation System (CRS) designed to assist farmers in making informed decisions regarding crop selection. The CRS utilizes machine learning algorithms and data analytics techniques to analyze various factors such as soil type, climate conditions, historical crop performance, market trends, and farmer preferences. Through the integration of satellite imagery, weather data, soil composition analysis, and historical crop yields, the CRS generates personalized recommendations tailored to specific agricultural contexts

Spatio-Temporal Detection of Land Use/Land Cover Changes in Kokrajhar District of Assam

The use of multi-temporal satellite images in digital change detection algorithms aids in the comprehension of landscape dynamics. The present study illustrates the spatio temporal dynamics of land use/land cover of Kokrajhar district of Assam, India. Landsat Satellite imageries of four different time periods. i.e., Landsat Thematic Mapper (TM) of 1991, 2001, 2011 and 2021 were acquired from Google Earth Explorer site and quantify the changes of Kokrajhar district from 1991 to 2021 over a period of 30 years. Supervised classification methodology has been employed using maximum likelihood technique in ArcMap 10.8 Software. The images of the study area were categorised into four different classes namely vegetation, agriculture, built up and water body. The results indicate that during the last three decades, built up have been increased by 3.8% (658.75 km2) while agriculture, vegetation and water body have been decreased by 0.74 (708.9 km2) %, 0.56(1494.46 km2) % and 2.46 (273.5 km2) % respectively.

A Case Study on Image Co-Registration of Hyper Spectral and Dual (L & S) Band SAR Data and Ore Findings Over Zewar Mines, India

The technique of superimposing two or more photographs in a way that ensures that for each image, the same pixel corresponds to the same location of the target scene is known as image coregistration It is a crucial stage in the picture enhancement process for satellite images. Different frequency bands store feature. Image fusion makes it possible to superimpose co-registered pictures taken by several sensors to get a superior image incorporating elements from both sources. On many match patches that are evenly dispersed over the two scenes, we estimate pixel offsets between possibly coherent picture pairings as image coregistration allows a more detailed single image to be obtained than many photos with distinct attributes. This study presents existing various fusion methods for ASAR (Airborne Synthetic Aperture Radar) images in the S-band and L-band to interpret urban, forestry, and agricultural areas. AVIRIS hyper spectral data also shows mining possibilities on ore of region. Hence, the seeking of ore region, and coregistration using fusion facilitates the remote sensing architecture next to drones.

Impacts of urban sprawl on people’s livelihoods: analysis of urban fringe neighbourhoods in Dar es Salaam, Tanzania

ABSTRACT Rapid urbanization rates and weak development control machinery have led to exponential city sprawling to urban fringes. This paper examines how urban sprawl impacts livelihoods of urban fringe dwellers and their coping strategies. A multiple case strategy was deployed and a sustainable livelihoods framework was used in the analysis. Empirical data were obtained through literature review, household and key informant interviews, observations, Focus Group Discussions and analysis of satellite images. Results indicate that negatively urban sprawl has increased households’ transport expenditure to work at unaffordable levels; traffic congestion; cost of food products; and inadequate provision of essential livelihood-enhancing facilities. Positively, urban sprawl has presented new livelihood opportunities and enabled households to access minimal social amenities and services. We recommend diversification of economic activities, change of economic activities, making urban fringe livelihoods key element of policy formulation and essential infrastructure and services provision to harness positive impacts and mitigate negative ones.

A new model for plasmapause locations derived from IMAGE RPI and Van Allen probe data

The outer boundary of the plasmasphere, the plasmapause, is characterized by a sharp electron density gradient that changes under varying space weather conditions. We developed a new model, called Neustrelitz/ESOC PlasmaPause Model (NEPPM), for providing plasmapause location in terms of -shell utilizing electron density measurements from the Van Allen probes from 2012 to 2018 and the IMAGE satellite data from 2001 to 2005. Both datasets were preprocessed, and algorithms were developed for automatic detection of plasmapause location. The suggested model provides a simple ellipse-based approach determined by the semi-major axis, the eccentricity, and the orientation angle of the semi-major axis. The modelled varies as a function of the index and magnetic local time . The NEPPM results are compared with the Global Core Plasma Model (GCPM). The plasmapause bulge in the evening hours follows the level of geomagnetic activity. The NEPPM will complete the NPSM (Neustrelitz PlasmaSphere Model), which was derived from dual-frequency GPS measurements on-board the CHAMP satellite mission.

Machine Learning for Identifying Emergent and Floating Aquatic Vegetation from Space: A Case Study in the Dniester Delta, Ukraine

Monitoring aquatic vegetation, including both floating and emergent types, plays a crucial role in understanding the dynamics of freshwater ecosystems. Our research focused on the Lower Dniester Basin in Southern Ukraine, covering approximately 1800 square kilometers of steppe plains and wetlands. We applied traditional machine learning algorithms, specifically random forest and boosting trees, to analyze Sentinel-2 satellite imagery for segmenting aquatic vegetation into emergent and floating types. Our methodology was validated against detailed in-situ field measurements collected annually over a 5-year study period. The machine learning classifiers achieved an F1-score of 0.88 ± 0.03 in classifying floating vegetation, outperforming our previously suggested histogram-based thresholding methodology for the same task. While emergent vegetation and open water were easily identifiable from satellite imagery, the robustness and temporal transferability of our methodology included accurately delineating floating vegetation as well. Additionally, we explored the significance of various features through the Minimum Redundancy - Maximum Relevance algorithm. This study highlights advancements in aquatic vegetation mapping and demonstrates a valuable tool for ecological monitoring and future research endeavors.