scholarly journals BUILDING EXTRACTION FROM REMOTE SENSING DATA USING FULLY CONVOLUTIONAL NETWORKS

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 481-486 ◽  
Author(s):  
K. Bittner ◽  
S. Cui ◽  
P. Reinartz
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Depth Information ◽  
Surface Model ◽  
Large Set ◽  
Building Detection ◽  
Sensing Applications ◽  
Fully Connected

Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a <i>Digital Surface Model (DSM)</i> using a Fully Convolution Network (FCN) architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM) as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF), which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.

scholarly journals Estimation of the temperature distribution in urban areas by using remote sensing data.

1994 ◽  
Vol 22 ◽  
pp. 267-273 ◽  
Author(s):  
Shinji KANEKO ◽  
Toshiie MAEDA ◽  
Takahito UENO ◽  
Hidefumi IMURA
Keyword(s):  
Remote Sensing ◽  
Temperature Distribution ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Sensing Data

A Digital Terrain Modeling Method in Urban Areas by the ICESat-2 (Generating precise terrain surface profiles from photon-counting technology)

2021 ◽  
Vol 87 (4) ◽  
pp. 237-248
Author(s):  
Nahed Osama ◽  
Bisheng Yang ◽  
Yue Ma ◽  
Mohamed Freeshah
Keyword(s):  
Urban Areas ◽  
Spatial Clustering ◽  
Photon Counting ◽  
Ground Surface ◽  
Ground Truth ◽  
Surface Model ◽  
Laser Altimeter ◽  
Ground Truth Data ◽  
Urban Scenes ◽  
Terrain Surface

The ICE, Cloud and land Elevation Satellite-2 (ICES at-2) can provide new measurements of the Earth's elevations through photon-counting technology. Most research has focused on extracting the ground and the canopy photons in vegetated areas. Yet the extraction of the ground photons from urban areas, where the vegetation is mixed with artificial constructions, has not been fully investigated. This article proposes a new method to estimate the ground surface elevations in urban areas. The ICES at-2 signal photons were detected by the improved Density-Based Spatial Clustering of Applications with Noise algorithm and the Advanced Topographic Laser Altimeter System algorithm. The Advanced Land Observing Satellite-1 PALSAR –derived digital surface model has been utilized to separate the terrain surface from the ICES at-2 data. A set of ground-truth data was used to evaluate the accuracy of these two methods, and the achieved accuracy was up to 2.7 cm, which makes our method effective and accurate in determining the ground elevation in urban scenes.

scholarly journals Improved Oriented Object Detection in Remote Sensing Images Based on a Three-Point Regression Method

2021 ◽  
Vol 13 (22) ◽  
pp. 4517
Author(s):  
Falin Wu ◽  
Jiaqi He ◽  
Guopeng Zhou ◽  
Haolun Li ◽  
Yushuang Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Poor Performance ◽  
Regression Method ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Bounding Box ◽  
Bounding Boxes ◽  
Fully Connected ◽  
Oriented Object

Object detection in remote sensing images plays an important role in both military and civilian remote sensing applications. Objects in remote sensing images are different from those in natural images. They have the characteristics of scale diversity, arbitrary directivity, and dense arrangement, which causes difficulties in object detection. For objects with a large aspect ratio and that are oblique and densely arranged, using an oriented bounding box can help to avoid deleting some correct detection bounding boxes by mistake. The classic rotational region convolutional neural network (R2CNN) has advantages for text detection. However, R2CNN has poor performance in the detection of slender objects with arbitrary directivity in remote sensing images, and its fault tolerance rate is low. In order to solve this problem, this paper proposes an improved R2CNN based on a double detection head structure and a three-point regression method, namely, TPR-R2CNN. The proposed network modifies the original R2CNN network structure by applying a double fully connected (2-fc) detection head and classification fusion. One detection head is for classification and horizontal bounding box regression, the other is for classification and oriented bounding box regression. The three-point regression method (TPR) is proposed for oriented bounding box regression, which determines the positions of the oriented bounding box by regressing the coordinates of the center point and the first two vertices. The proposed network was validated on the DOTA-v1.5 and HRSC2016 datasets, and it achieved a mean average precision (mAP) of 3.90% and 15.27%, respectively, from feature pyramid network (FPN) baselines with a ResNet-50 backbone.

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

scholarly journals Groundwater Potential Zones in Relation to Catchment Condition in Orenburg, Russia

2019 ◽  
pp. 60-65
Author(s):  
K Choudhary ◽  
M S Boori ◽  
A Kupriyanov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Groundwater Potential ◽  
Groundwater Potential Zone ◽  
Potential Zone ◽  
Ground Truth Data ◽  
Groundwater Availability ◽  
Sensing Data

The main objective of this study was to detect groundwater availability for agriculture in the Orenburg, Russia. Remote sensing data (RS) and geographic information system (GIS) were used to locate potential zones for groundwater in Orenburg. Diverse maps such as a base map, geomorphological, geological structural, lithology, drainage, slope, land use/cover and groundwater potential zone were prepared using the satellite remote sensing data, ground truth data, and secondary data. ArcGIS software was utilized to manipulate these data sets. The groundwater availability of the study was classified into different classes such as very high, high, moderate, low and very low based on its hydro-geomorphological conditions. The land use/cover map was prepared using a digital classification technique with the limited ground truth for mapping irrigated areas in the Orenburg, Russia.

Have vehicle emissions of primary NO2 peaked?

2016 ◽  
Vol 189 ◽  
pp. 439-454 ◽  
Author(s):  
David C. Carslaw ◽  
Tim P. Murrells ◽  
Jon Andersson ◽  
Matthew Keenan
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Vehicle Emissions ◽  
Remote Sensing Data ◽  
Vehicle Emission ◽  
Oxides Of Nitrogen ◽  
Vehicle Exhaust ◽  
Road Vehicles ◽  
Limit Values ◽  
Sensing Data

Reducing ambient concentrations of nitrogen dioxide (NO2) remains a key challenge across many European urban areas, particularly close to roads. This challenge mostly relates to the lack of reduction in emissions of oxides of nitrogen (NOx) from diesel road vehicles relative to the reductions expected through increasingly stringent vehicle emissions legislation. However, a key component of near-road concentrations of NO2 derives from directly emitted (primary) NO2 from diesel vehicles. It is well-established that the proportion of NO2 (i.e. the NO2/NOx ratio) in vehicle exhaust has increased over the past decade as a result of vehicle after-treatment technologies that oxidise carbon monoxide and hydrocarbons and generate NO2 to aid the emissions control of diesel particulate. In this work we bring together an analysis of ambient NOx and NO2 measurements with comprehensive vehicle emission remote sensing data obtained in London to better understand recent trends in the NO2/NOx ratio from road vehicles. We show that there is evidence that NO2 concentrations have decreased since around 2010 despite less evidence of a reduction in total NOx. The decrease is shown to be driven by relatively large reductions in the amount of NO2 directly emitted by vehicles; from around 25 vol% in 2010 to 15 vol% in 2014 in inner London, for example. The analysis of NOx and NO2 vehicle emission remote sensing data shows that these reductions have been mostly driven by reduced NO2/NOx emission ratios from heavy duty vehicles and buses rather than light duty vehicles. However, there is also evidence from the analysis of Euro 4 and 5 diesel passenger cars that as vehicles age the NO2/NOx ratio decreases. For example the NO2/NOx ratio decreased from 29.5 ± 2.0% in Euro 5 diesel cars up to one year old to 22.7 ± 2.5% for four-year old vehicles. At some roadside locations the reductions in primary NO2 have had a large effect on reducing both the annual mean and number of hourly exceedances of the European Limit Values of NO2.

scholarly journals Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle

2018 ◽  
Vol 48 (6) ◽  
Author(s):  
Du Wen ◽  
Xu Tongyu ◽  
Yu Fenghua ◽  
Chen Chunling
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Unmanned Aerial Vehicle ◽  
Gaussian Process Regression ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Ground Truth ◽  
Hyperspectral Remote Sensing ◽  
Inversion Algorithm ◽  
Aerial Vehicle

ABSTRACT: The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.

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