Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

scholarly journals Mapping and monitoring of the land use/cover changes in the wider area of Itanos, Crete, using very high resolution EO imagery with specific interest in archaeological sites

2020 ◽  
Author(s):  
Alexander R. Brown ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Swaiti Suman
Keyword(s):  
High Resolution ◽  
Image Data ◽  
Archaeological Site ◽  
Archaeological Sites ◽  
Large Area ◽  
Open Spaces ◽  
Site Mapping ◽  
Object Based ◽  
Rule Sets ◽  
Rule Set

Archaeological site mapping is important for both understanding the history and protectingthe sites from excavation during developmental activities. As archaeological sites aregenerally spread over a large area, use of high spatial resolution remote sensing imageryis becoming increasingly applicable in the world. The main objective of this study is tomap the land cover of the Itanos area of Crete and of its changes, with specific focus onthe detection of the landscape’s archaeological features. Six satellite images were acquiredfrom the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digitalimagery of two known archaeological sites was used for validation. An object-based imageanalysis classification was subsequently developed using the five acquired satellite images.Two rule sets were created, one using the standard four bands which both satellites haveand another for the two WorldView-2 images with their four extra bands included. Validationof the thematic maps produced from the classification scenarios confirmed a differencein accuracy amongst the five images. Comparing the results of a 4-band rule set versusthe 8-band rule set showed a slight increase in classification accuracy using extra bands.The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separatingthe archaeological sites from the open spaces with little or no vegetation proved tobe challenging. This was mainly due to the high spectral similarity between rocks and thearchaeological ruins. The high resolution of the satellite data allowed for the accuracy indefining larger archaeological sites, but still there was difficulty in distinguishing smallerareas of interest. The digital image data provided a very good 3D representation for thearchaeological sites, assisting as well as in validating the satellite-derived classificationmaps. To conclude, our study provides further evidence that use of high resolution imagerymay allow for archaeological sites to be located, but only where the archaelogical featuresare of an adequate size.

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

scholarly journals ALGORITHM AND APPLICATION OF GCP-INDEPENDENT BLOCK ADJUSTMENT FOR SUPER LARGE-SCALE DOMESTIC HIGH RESOLUTION OPTICAL SATELLITE IMAGERY

2018 ◽  
Vol XLII-3 ◽  
pp. 1607-1615
Author(s):  
Y. S. Sun ◽  
L. Zhang ◽  
B. Xu ◽  
Y. Zhang
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Optimization Method ◽  
Adjustment Process ◽  
Large Area ◽  
Rank Defect ◽  
And Performance

The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image – GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.

scholarly journals Classifying settlement types from multi-scale spatial patterns of building footprints

2020 ◽  
pp. 239980832092120 ◽  
Author(s):  
Warren C Jochem ◽  
Douglas R Leasure ◽  
Oliver Pannell ◽  
Heather R Chamberlain ◽  
Patricia Jones ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Spatial Patterns ◽  
Gaussian Mixture ◽  
Developed Countries ◽  
Urban Morphology ◽  
Multi Scale ◽  
High Resolution Imagery ◽  
Very High Resolution Imagery ◽  
Very High

Urban settlements and urbanised populations continue to grow rapidly and much of this transition is occurring in less developed countries. Remote sensing techniques are now often applied to monitor urbanisation and changes in settlement patterns. In particular, increasing availability of very high resolution imagery (<1 m spatial resolution) and computing power is enabling complete sets of settlement data in the form of building footprints to be extracted from imagery. These settlement data provide information on the changes occurring in cities, particularly in countries which may lack other data on urbanisation. While spatially detailed, extracted building footprints typically lack other information that identify building types or can be used to differentiate intra-urban land uses or neighbourhood types. This work demonstrates an approach to classifying settlement types through multi-scale spatial patterns of urban morphology visible in building footprint data extracted from very high resolution imagery. The work uses a Gaussian mixture modelling approach to select and hierarchically merge components into clusters. The results are maps classifying settlement types on a high spatial resolution (100 m) grid. The approach is applied in Kaduna, Nigeria; Kinshasa, Democratic Republic of the Congo; and Maputo, Mozambique and demonstrates the potential of computational methods to take advantage of large spatial datasets and extract meaningful information to support monitoring of urban areas. The model-based approach produces a hierarchy of potential clustering solutions, and we suggest that this can be used in partnership with local knowledge of the context when creating settlement typologies.

scholarly journals High Resolution Drone Surveying of the Pista Geoglyph in Palpa, Peru

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 479 ◽  
Author(s):  
Karel Pavelka ◽  
Jaroslav Šedina ◽  
Eva Matoušková
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Current Knowledge ◽  
Super Resolution ◽  
Archaeological Sites ◽  
Historical Objects ◽  
Fine Structures ◽  
Very High ◽  
Resolution Data

Currently, satellite images can be used to document historical or archaeological sites in areas that are distant, dangerous, or expensive to visit, and they can be used instead of basic fieldwork in several cases. Nowadays, they have final resolution on 35–50 cm, which can be limited for searching of fine structures. Results using the analysis of very high resolution (VHR) satellite data and super resolution data from drone on an object nearby Palpa, Peru are discussed in this article. This study is a part of Nasca project focused on using satellite data for documentation and the analysis of the famous geoglyphs in Peru near Palpa and Nasca, and partially on the documentation of other historical objects. The use of drone shows advantages of this technology to achieve high resolution object documentation and analysis, which provide new details. The documented site was the “Pista” geoglyph. Discovering of unknown geoglyphs (a bird, a guinea pig, and other small drawings) was quite significant in the area of the well-known geoglyph. The new data shows many other details, unseen from the surface or from the satellite imagery, and provides the basis for updating current knowledge and theories about the use and construction of geoglyphs.

scholarly journals Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 230
Author(s):  
Sultan Daud Khan ◽  
Louai Alarabi ◽  
Saleh Basalamah
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Multiple Scales ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Hybrid Network ◽  
High Resolution Satellite Images

Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a large number of high-resolution satellite images are readily available. However, these high-resolution satellite images are complex due to increased spatial resolution and data disruption caused by different factors involved in the acquisition process. Due to these challenges, an efficient land-cover semantic segmentation model is difficult to design and develop. In this paper, we develop a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net. This is carried out to obtain a pixel-wise classification of land cover. The contraction path of U-Net is replaced with DenseNet to extract features of multiple scales, while long-range connections of U-Net concatenate encoder and decoder paths are used to preserve low-level features. We evaluate the proposed hybrid network on a challenging, publicly available benchmark dataset. From the experimental results, we demonstrate that the proposed hybrid network exhibits a state-of-the-art performance and beats other existing models by a considerable margin.

scholarly journals Detection of Oil Tank from High Resolution Remote Sensing Images using Morphological and Statistical Tools

2021 ◽  
Author(s):  
D. Chaudhuri ◽  
I. Sharif
Keyword(s):  
High Resolution ◽  
Domain Knowledge ◽  
Minimum Distance ◽  
Satellite Images ◽  
Remote Sensing Images ◽  
Important Target ◽  
Open Research ◽  
High Resolution Imagery ◽  
Thinning Operations ◽  
Oil Tank

Oil tank is an important target and automatic detection of the target is an open research issue in satellite based high resolution imagery. This could be used for disaster screening, oil outflow, etc. A new methodology has been proposed for consistent and precise automatic oil tank detection from such panchromatic images. The proposed methodology uses both spatial and spectral properties domain knowledge regarding the character of targets in the sight. Multiple steps are required for detection of the target in the methodology – 1) enhancement technique using directional morphology, 2) multi-seed based clustering procedure using internal gray variance (IGV), 3) binarization and thinning operations, 4) circular shape detection by Hough transform, 5) MST based special relational grouping operation and 6) supervised minimum distance classifier for oil tank detection. IKONOS and Quickbird satellite images are used for testing the proposed algorithm. The outcomes show that the projected methodology in this paper is both precise and competent.

scholarly journals Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

2020 ◽  
Vol 12 (24) ◽  
pp. 4158
Author(s):  
Mengmeng Li ◽  
Alfred Stein
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Arrangement ◽  
Remote Sensing Images ◽  
Land Use Types ◽  
High Resolution Satellite Images

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.

scholarly journals DISTRIBUIÇÃO ESPACIAL E ARQUEOLOGIA DA PAISAGEM: O Sítio Arqueológico El Socorro, um Padrão Atípico Mesoamericano

2019 ◽  
Vol 33 (3) ◽  
pp. 165
Author(s):  
Pedro Jiménez Lara ◽  
Carlos Fabiano Marques de Lima
Keyword(s):  
Spatial Distribution ◽  
Satellite Images ◽  
Complex Analysis ◽  
Archaeological Site ◽  
Archaeological Sites ◽  
Contextual Approach ◽  
Archaeological Data ◽  
The City ◽  
Present Text ◽  
The Way

O presente texto surgiu como um desafio, a análise de um assentamento mesoamericano pré-hispânico, denominado El Socorro, localizado próximo a cidade de Tlacojalpan em Veracruz, México, região do Golfo da Mesoamerica. A realização de uma abordagem contextual dos sítios arqueológicos, numa perspectiva intra e extra sítio, num primeiro momento fazendo uso de ferramentas de geoprocessamento e de procedimentos topográficos com estação total. Optamos nesse texto utilizar como substrato para nossas observações e avaliação das imagens de satélite, desenhos topográficos e dados arqueológicos dos sítios pré-hispânicos uma abordagem centrada na arqueologia da paisagem para uma identificação em superficie mas ampla do sitio dentro do contexto regional e mesoamericano. Um analisis complexo por tratarse de um sitio excepcional pela distribuçao e forma como foi construído. SPATIAL DISTRIBUTION AND LANDSCAPE ARCHEOLOGY: The Socorro Archaeological Site, The El Socorro Archaeological Site, an Atypical Mesoamerican PatternABSTRACTThe present text appeared as a challenge, the analysis of a pre-Hispanic Mesoamerican settlement, named El Socorro, located near the city of Tlacojalpan in Veracruz, Mexico. region of the Gulf of Mesoamerica. The realization of a contextual approach of archaeological sites, in an intra and extra-site perspective, at first using geoprocessing tools and topographic procedures. We chose to use as substrate for our observations and evaluation of satellite images, topographical drawings and archaeological data of the prehispanic sites, an approach centered on landscape archeology for a broader surface identification of the site within the regional and Mesoamerican context. A complex analysis because it is an exceptional site for the distribution and the way it was buil.Keywords: El Socorro; Mesoamerica; Archaeological Site; Landscape 

scholarly journals TOWARDS AN AUTOMATED FLOOD AREA EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 97-104
Author(s):  
R. G. C. J. Kapilaratne ◽  
S. Kaneta
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Global Climate ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Flood Events ◽  
Large Area ◽  
High Resolution Satellite Images ◽  
Catastrophic Impact

Abstract. Flooding is considered as one of the most devastated natural disasters due to its adverse effect on human lives as well as economy. Since more population concentrate towards flood prone areas and frequent occurrence of flood events due to global climate change, there is an urgent need in remote sensing community for faster and reliable inundation mapping technologies to increase the preparedness of population and reduce the catastrophic impact. With the recent advancement in remote sensing technologies and integration capability of deep learning algorithms with remote sensing data makes faster mapping of large area is feasible. Therefore, this study attempted to explore a faster and low cost solution for flood area extraction by integrating convolution neural networks (CNNs) with high resolution (1.5 m) SPOT satellite images. By consider the system requirement as a measure of cost, capabilities (speed and accuracy) of a deeper (ResNet101) and a shallower (MobileNetV2) CNNs on flood mapping were examined and compared. The models were trained and tested with satellite images captured during several flood events occurred in Japan. It is observed from the results that ResNet101 obtained better flood area mapping accuracy than MobileNetV2. Whereas, MobileNetV2 is having much higher capabilities in faster mapping in 0.3 s/km2 with a competitive accuracy and minimal system requirements than ResNet101.

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