scholarly journals Potential of Pléiades and WorldView-3 Tri-Stereo DSMs to Represent Heights of Small Isolated Objects

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2695 ◽  
Author(s):  
Ana-Maria Loghin ◽  
Johannes Otepka-Schremmer ◽  
Norbert Pfeifer
Keyword(s):  
High Resolution ◽  
3D Reconstruction ◽  
Surface Model ◽  
Object Type ◽  
Reconstruction Process ◽  
High Resolution Imagery ◽  
Dense Image ◽  
Very High Resolution Imagery ◽  
Actual Height ◽  
Very High

High-resolution stereo and multi-view imagery are used for digital surface model (DSM) derivation over large areas for numerous applications in topography, cartography, geomorphology, and 3D surface modelling. Dense image matching is a key component in 3D reconstruction and mapping, although the 3D reconstruction process encounters difficulties for water surfaces, areas with no texture or with a repetitive pattern appearance in the images, and for very small objects. This study investigates the capabilities and limitations of space-borne very high resolution imagery, specifically Pléiades (0.70 m) and WorldView-3 (0.31 m) imagery, with respect to the automatic point cloud reconstruction of small isolated objects. For this purpose, single buildings, vehicles, and trees were analyzed. The main focus is to quantify their detectability in the photogrammetrically-derived DSMs by estimating their heights as a function of object type and size. The estimated height was investigated with respect to the following parameters: building length and width, vehicle length and width, and tree crown diameter. Manually measured object heights from the oriented images were used as a reference. We demonstrate that the DSM-based estimated height of a single object strongly depends on its size, and we quantify this effect. Starting from very small objects, which are not elevated against their surroundings, and ending with large objects, we obtained a gradual increase of the relative heights. For small vehicles, buildings, and trees (lengths <7 pixels, crown diameters <4 pixels), the Pléiades-derived DSM showed less than 20% or none of the actual object’s height. For large vehicles, buildings, and trees (lengths >14 pixels, crown diameters >7 pixels), the estimated heights were higher than 60% of the real values. In the case of the WorldView-3 derived DSM, the estimated height of small vehicles, buildings, and trees (lengths <16 pixels, crown diameters <8 pixels) was less than 50% of their actual height, whereas larger objects (lengths >33 pixels, crown diameters >16 pixels) were reconstructed at more than 90% in height.

scholarly journals Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2508
Author(s):  
Loredana Oreti ◽  
Diego Giuliarelli ◽  
Antonio Tomao ◽  
Anna Barbati
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Parametric Method ◽  
Mixed Forest ◽  
Mixed Stands ◽  
Mapping Unit ◽  
Mixed Forests ◽  
High Resolution Imagery ◽  
Very High Resolution Imagery ◽  
Very High

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

scholarly journals Building Extraction in Very High Resolution Imagery by Dense-Attention Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1768 ◽  
Author(s):  
Hui Yang ◽  
Penghai Wu ◽  
Xuedong Yao ◽  
Yanlan Wu ◽  
Biao Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Building Extraction ◽  
Learning Networks ◽  
Feature Maps ◽  
Low Level ◽  
High Resolution Imagery ◽  
Very High Resolution Imagery ◽  
High Level ◽  
Very High

Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this task by using both high-level and low-level feature maps. However, it is difficult to utilize different level features rationally with the present deep learning networks. To tackle this problem, a novel network based on DenseNets and the attention mechanism was proposed, called the dense-attention network (DAN). The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module. The proposed encoder–decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only red–green–blue (RGB) images demonstrated that the proposed DAN achieved a higher score (96.16% overall accuracy (OA), 92.56% F1 score, 90.56% mean intersection over union (MIOU), less training and response time and higher-quality value) when compared with other deep learning methods.

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.

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