scholarly journals An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiao Xie ◽  
Xiran Zhou ◽  
Jingzhong Li ◽  
Weijiang Dai
Keyword(s):  
Remote Sensing ◽  
Object Recognition ◽  
Probabilistic Models ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Low Level ◽  
Level Data ◽  
Major Disadvantage ◽  
High Level

Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human’s semantics on the object to be recognized are required more explorations. Moreover, interpretability of feature extraction becomes a major disadvantage of the state-of-the-art CNN. Especially for the complex urban objects, which varies in geometrical shapes, functional structures, environmental contexts, etc, due to the heterogeneity between low-level data features and high-level semantics, the features derived from remote sensing data alone are limited to facilitate an accurate recognition. In this paper, we present an ontology-based methodology framework for enabling object recognition through rules extracted from the high-level semantics, rather than unexplainable features extracted from a CNN. Firstly, we semantically organize the descriptions and definitions of the object as semantics (RDF-triple rules) through our developed domain ontology. Secondly, we exploit semantic web rule language to propose an encoder model for decomposing the RDF-triple rules based on a multilayer strategy. Then, we map the low-level data features, which are defined from optical satellite image and LiDAR height, to the decomposed parts of RDF-triple rules. Eventually, we apply a probabilistic belief network (PBN) to probabilistically represent the relationships between low-level data features and high-level semantics, as well as a modified TanH function is used to optimize the recognition result. The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning.

scholarly journals BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA

2016 ◽  
Vol III-7 ◽  
pp. 83-88 ◽  
Author(s):  
M. Papadomanolaki ◽  
M. Vakalopoulou ◽  
S. Zagoruyko ◽  
K. Karantzalos
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Multispectral Data ◽  
Sensing Data ◽  
Multispectral Remote Sensing ◽  
High Level ◽  
Learning Frameworks

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the <i>AlexNet</i>, <i>AlexNet-small</i> and <i>VGG</i> models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates <i>i.e.</i>, above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

scholarly journals Aplikasi Penghitungan Gross Primary Production dari Data Penginderaan Jauh

2016 ◽  
pp. 31
Author(s):  
Komang Gede Kurniadi ◽  
I Putu Agung Bayupati ◽  
I Dewa Nyoman Nurweda Putra
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Automatic Data ◽  
Sensing Data ◽  
Metadata File ◽  
Production Calculation

Calculation of Gross Primary Production that utilize remote sensing data is can be done on commercial remote sensing software by manual method. The commercial remote sensing software does not provides a specific feature that allow the user to do the Gross Primary Production calculation. This research is aimed to to build a remote sensing software that can be specifically used to do the Gross Primary Production calculation for Denpasar area. This software accepts remote sensing data as an input, such as satellite image from Landsat 8 OLI and TIRS and metadata file. The formulas and supporting data that required on the Gross Primary Production calculation are implemented on software in order to make an automatic image processing software. There also some additional feature on this software such as automatic data parsing from metadata file, cropping, masking and zoom that could help user to do the Gross Primary Production calculation. The developed software is able to produce information such as Gross Primary Production  value that depicted by a figure with color segmentation, area of the segments and mean, minimum and maximum value of the Gross Primary Production.  

scholarly journals RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 565-568
Author(s):  
N. Fu ◽  
L. Sun ◽  
H. Z. Yang ◽  
J. Ma ◽  
B. Q. Liao
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Management System ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Database Management System ◽  
Sensor Data ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Effective Organization

Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.

scholarly journals BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA

2016 ◽  
Vol III-7 ◽  
pp. 83-88 ◽  
Author(s):  
M. Papadomanolaki ◽  
M. Vakalopoulou ◽  
S. Zagoruyko ◽  
K. Karantzalos
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Multispectral Data ◽  
Sensing Data ◽  
Multispectral Remote Sensing ◽  
High Level ◽  
Learning Frameworks

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the <i>AlexNet</i>, <i>AlexNet-small</i> and <i>VGG</i> models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates <i>i.e.</i>, above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

scholarly journals Towards a Satellite System for Archaeology? Simulation of an Optical Satellite Mission with Ideal Spatial and Temporal Resolution, Illustrated by a Case Study in Scotland

2020 ◽  
Vol 12 (24) ◽  
pp. 4100
Author(s):  
Ciara N. McGrath ◽  
Charlie Scott ◽  
Dave Cowley ◽  
Malcolm Macdonald
Keyword(s):  
Remote Sensing ◽  
Temporal Resolution ◽  
Simulation Analysis ◽  
Remote Sensing Data ◽  
Satellite System ◽  
Satellite Mission ◽  
Archaeological Prospection ◽  
Sensing Data ◽  
High Level

Applications of remote sensing data for archaeology rely heavily on repurposed data, which carry inherent limitations in their suitability to help address archaeological questions. Through a case study framed around archaeological imperatives in a Scottish context, this work investigates the potential for existing satellite systems to provide remote sensing data that meet defined specifications for archaeological prospection, considering both spatial and temporal resolution, concluding that the availability of commercial data is currently insufficient. Tasking a commercial constellation of 12 spacecraft to collect images of a 150 km2 region in Scotland through the month of July 2020 provided 26 images with less than 50% cloud cover. Following an analysis of existing systems, this paper presents a high-level mission architecture for a bespoke satellite system designed from an archaeological specification. This study focuses on orbit design and the number of spacecraft needed to meet the spatial and temporal resolution requirements for archaeological site detection and monitoring in a case study of Scotland, using existing imaging technology. By exploring what an ideal scenario might look like from a satellite mission planning perspective, this paper presents a simulation analysis that foregrounds archaeological imperatives and specifies a satellite constellation design on that basis. High-level design suggests that a system of eight 100 kg spacecraft in a 581 km altitude orbit could provide coverage at a desired temporal and spatial resolution of two-weekly revisit and <1 m ground sampling distance, respectively. The potential for such a system to be more widely applied in regions of similar latitude and climate is discussed.

scholarly journals The use of Landsat 8 remote sensing data for assessing the temperature conditions of the Larus ridibundus L. habitat

2020 ◽  
Vol 9 (4) ◽  
pp. 184-191
Author(s):  
Sergey Arkadyevich Shurakov ◽  
Aleksey Nikolaevich Chashchin
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Close Correlation ◽  
Primary Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Temperature Conditions

This paper discusses the possibilities of using Landsat 8 remote sensing data for assessing the temperature conditions of aquatic landscapes when studying the abundance and density of gulls. The study of the ornithological situation was carried out on the territory of the Perm international airport of the Perm Region, where the black-headed gull is an unfavorable factor in the safety of passenger aircraft flights. Within the boundaries of the region, 5 reservoirs were identified. A method for calculating the surface temperature from a multispectral satellite image of the Landsat 8 series is described in detail with the presentation of primary data sources, atmospheric parameters and obtaining raster coverage with a resolution of 30 meters per pixel. The tool used for the calculation is the Land Surface Temperature module of the QGIS software. The paper presents maps of temperature within the area of conducted ornithological surveys and the density of gulls. The densities of birds for individual bodies of water are calculated using the Spatial Analyst module of the ArcGIS program with the kernel density tool. According to the research results, a close correlation was established between the attractiveness of reservoirs for gulls and water temperature. The correlation coefficients were 0,83 and 0,71, respectively, with the abundance and density of gulls.

scholarly journals Recognition of forest and shrub communities on the base of remotely sensed data supported by ground studies

2019 ◽  
pp. 233-242
Author(s):  
A Y Denisova ◽  
L M Kavelenova ◽  
E S Korchikov ◽  
A V Pomogaybin ◽  
N V Prokhorova ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wide Spectrum ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Steppe ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Multispectral Remote Sensing ◽  
Selection For ◽  
High Level

The forest and shrub communities are important components of the environment and provide a wide spectrum of ecological services. In the Samara region the forest and shrub cover is dispersed on the territory what makes its monitoring difficult. The forest areas are limited by natural and anthropogenic reasons since Samara region is a forest-steppe territory with a high level of human activity. The shrub communities are mostly the secondary ecosystems incorporated in natural grassy communities, agricultural fields or enclosing to forests. These specific ecosystems can be recognized on remote sensing data including satellite images supported by preliminary ground surveys. In this article, we present the study of the forest and shrub communities recognition using remote sensing images and ground surveys in the Samara region. We describe a process of the test site selection for remote sensing data verification and discuss the results of applying the author’s classification technology for multispectral remote sensing composites to classify forest communities in the Samara region

scholarly journals Assessment of impact of mass movements on the upper Tayyah valley's bridge along Shear escarpment highway, Asir region (Saudi Arabia) using remote sensing data and field investigation

2015 ◽  
Vol 3 (1) ◽  
pp. 497-533 ◽  
Author(s):  
A. M. Youssef ◽  
M. Al-Kathery ◽  
B. Pradhan
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Negative Impact ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Shuttle Radar Topography Mission ◽  
Field Investigation ◽  
Running Water ◽  
Sensing Data ◽  
Heavy Trucks

Abstract. Escarpment highways, roads and mountainous areas in Saudi Arabia are facing landslide hazards that are frequently occurring from time to time causing considerable damage to these areas. Shear escarpment highway is located in the north of the Abha city. It is the most important escarpment highway in the area, where all the light and heavy trucks and vehicle used it as the only corridor that connects the coastal areas in the western part of the Saudi Arabia with the Asir and Najran Regions. More than 10 000 heavy trucks and vehicles use this highway every day. In the upper portion of Tayyah valley of Shear escarpment highway, there are several landslide and erosion potential zones that affect the bridges between tunnel 7 and 8 along the Shear escarpment Highway. In this study, different types of landslides and erosion problems were considered to access their impacts on the upper Tayyah valley's bridge along Shear escarpment highway using remote sensing data and field investigation. These landslides and erosion problems have a negative impact on this section of the highway. Results indicate that the areas above the highway and bridge level between bridge 7 and 8 have different landslides including planar, circular, rockfall failures and debris flows. In addition, running water through the gullies cause different erosional (scour) features between and surrounding the bridge piles and culverts. A detailed landslides and erosion features map was created based on intensive field investigation (geological, geomorphological, and structural analysis), and interpretation of Landsat image 15 m and high resolution satellite image (QuickBird 0.61 m), shuttle radar topography mission (SRTM 90 m), geological and topographic maps. The landslides and erosion problems could exhibit serious problems that affect the stability of the bridge. Different mitigation and remediation strategies have been suggested to these critical sites to minimize and/or avoid these problems in the future.

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