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.

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 Feature Extraction and Content Based Image Retrieval for High Resolution Remote Sensing Images

2019 ◽  
Vol 8 (3) ◽  
pp. 8881-8884
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Image Retrieval ◽  
Satellite Images ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Remote Sensing Images ◽  
Image Collection

These are the days where we are very rich in information and poor in data. This is very true in case of image data. Whether it is the case of normal images or satellite images, the image collection is very huge but utilizing those images is of least concern. Extracting features from big images is a very challenging and compute intensive task but if we realize it, it will be very fruitful. CBIR (Content Based Image Retrieval) when used with HRRS (High Resolution Remote Sensing) images will yield with effective data.

scholarly journals The Analysis of Geospatial Information for Validating Some Numbers of Islands in Indonesia

2017 ◽  
Vol 49 (2) ◽  
pp. 204 ◽  
Author(s):  
Sukendra - Martha
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Geospatial Data ◽  
The Other ◽  
Remote Sensing Images ◽  
Geospatial Information ◽  
Internal Affair ◽  
High Resolution Satellite Images ◽  
Data Source

This article discusses a comparison of various numbers of islands in Indonesia; and it addresses a valid method of accounting or enumerating numbers of islands in Indonesia. Methodology used is an analysis to compare the different number of islands from various sources.  First, some numbers of  Indonesian islands were derived from: (i) Centre for Survey and Mapping- Indonesian Arm Forces (Pussurta ABRI) recorded as 17,508 islands; (ii) Agency for Geospatial Information (BIG) previously known as National Coordinating Agency for Surveys and Mapping (Bakosurtanal) as national mapping authority reported with 17,506 islands (after loosing islands of  Sipadan and Ligitan); (iii) Ministry of Internal Affair published 17,504 islands. Many parties have referred the number of 17,504 islands even though it has not yet been supported by back-up documents; (iv) Hidrographic Office of Indonesian Navy has released with numbers of 17,499; (v) Other sources indicated different numbers of islands, and indeed will imply to people confusion. In the other hand, the number of 13,466 named islands has a strong document (Gazetteer). Second, enumerating the total number of islands in Indonesia can be proposed by three ways: (i) island census through toponimic survey, (ii) using map, and (iii) applying remote sensing images. Third, the procedures of searching valid result in number of islands is by remote sensing approach - high resolution satellite images. The result of this work implies the needs of one geospatial data source (including total numbers of islands) in the form of ‘One Map Policy’ that will impact in the improvement of  Indonesian geographic data administration. 

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.

Multi-phase Oil Tank Recognition for High Resolution Remote Sensing Images

2018 ◽  
Vol 24 (3) ◽  
pp. 663-670
Author(s):  
Changjiang Liu ◽  
Xuling Wu ◽  
Bing Mo ◽  
Yi Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Images ◽  
Oil Tank ◽  
Multi Phase

scholarly journals Controllable Fused Semantic Segmentation with Adaptive Edge Loss for Remote Sensing Parsing

2022 ◽  
Vol 14 (1) ◽  
pp. 207
Author(s):  
Xudong Sun ◽  
Min Xia ◽  
Tianfang Dai
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Loss Function ◽  
Satellite Images ◽  
Semantic Segmentation ◽  
Image Understanding ◽  
High Complexity ◽  
Remote Sensing Images ◽  
Coupled Mode ◽  
Edge Features

High-resolution remote sensing images have been put into the application in remote sensing parsing. General remote sensing parsing methods based on semantic segmentation still have limitations, which include frequent neglect of tiny objects, high complexity in image understanding and sample imbalance. Therefore, a controllable fusion module (CFM) is proposed to alleviate the problem of implicit understanding of complicated categories. Moreover, an adaptive edge loss function (AEL) was proposed to alleviate the problem of the recognition of tiny objects and sample imbalance. Our proposed method combining CFM and AEL optimizes edge features and body features in a coupled mode. The verification on Potsdam and Vaihingen datasets shows that our method can significantly improve the parsing effect of satellite images in terms of mIoU and MPA.

scholarly journals Using different satellite imagery and classification techniques to assess the contribution of trees outside forests in the municipality of Maringá, Brazil

Revista CERES ◽  
2013 ◽  
Vol 60 (4) ◽  
pp. 480-488 ◽  
Author(s):  
Betina Doubrawa ◽  
Ana Paula Dalla Corte ◽  
Carlos Roberto Sanquetta
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Forest Cover ◽  
Object Oriented ◽  
Forest Fragments ◽  
Trees Outside Forests ◽  
Size Classes ◽  
High Resolution Imagery ◽  
Medium Resolution ◽  
Vegetation Patches

Forest cover of the Maringá municipality, located in northern Parana State, was mapped in this study. Mapping was carried out by using high-resolution HRC sensor imagery and medium resolution CCD sensor imagery from the CBERS satellite. Images were georeferenced and forest vegetation patches (TOFs - trees outside forests) were classified using two methods of digital classification: reflectance-based or the digital number of each pixel, and object-oriented. The areas of each polygon were calculated, which allowed each polygon to be segregated into size classes. Thematic maps were built from the resulting polygon size classes and summary statistics generated from each size class for each area. It was found that most forest fragments in Maringá were smaller than 500 m². There was also a difference of 58.44% in the amount of vegetation between the high-resolution imagery and medium resolution imagery due to the distinct spatial resolution of the sensors. It was concluded that high-resolution geotechnology is essential to provide reliable information on urban greens and forest cover under highly human-perturbed landscapes.

scholarly journals Informative accuracy investigation and updating map using remote sensing technique and GIS

2018 ◽  
Vol 15 (35) ◽  
pp. 169-187
Author(s):  
Nawal K. Ghazal
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Topographic Maps ◽  
Geometric Correction ◽  
Topographic Map ◽  
Geometric Accuracy ◽  
Remote Sensing Technique ◽  
High Resolution Imagery ◽  
High Resolution Satellite Imagery

In this work, using GPS which has best accuracy that can be established set of GCPs, also two satellite images can be used, first with high resolution QuickBird, and second has low resolution Landsat image and topographic maps with 1:100,000 and 1:250,000 scales. The implementing of these factors (GPS, two satellite images, different scales for topographic maps, and set of GCPs) can be applying. In this study, must be divided this work into two parts geometric accuracy and informative accuracy investigation. The first part is showing geometric correction for two satellite images and maps.The second part of the results is to demonstrate the features (how the features appearance) of topographic map or pictorial map (image map), Where it is highlight the different features with different scales to know the accuracy of information. Where can be noticed through graphics that features appear very close to each other at a certain scale and become detached at another scale and this problem can be solved by generalization method. Geodatabase in GIS program also have been used as a modern style store all data related to the project in one folder divisions with that data. In addition to storage in a shape file. Geographic Information Systems (GIS) uses Remote Sensing (RS) data for a lot of applications. One of the application areas is the updating of the GIS database using high resolution imagery. Finally, high resolution satellite imagery data is very important to obtain updating map for Kut city by implementation two methods geodatabase and shapefile.

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