Optimisation of global grids for high-resolution remote sensing data

Bauer-Marschallinger, Bernhard; Sabel, Daniel; Wagner, Wolfgang

doi:10.1016/j.cageo.2014.07.005

Cited by 67 publications

(59 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this method we have new points instead of points with the original coordinates (Gościewski, 2013;Bauer-Marschallinger et al, 2014).…”

Section: Generationmentioning

confidence: 99%

Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

Błaszczak-Bąk¹

2016

Acta Geodynamica Et Geomaterialia

View full text Add to dashboard Cite

The number of point clouds provided by LiDAR technology can be sometimes seen as a problem in development and further processing for given purposes (e.g. Digital Terrain Model (DTM) generation). Therefore, there is still a need to reduce the obtained big datasets. Reducing can be done, inter alia, by reducing the size of the set or by generating the set. This paper presents two variants of the reduction of point clouds in order to effectively generate the DTM. There is also a comparison of two DTMs generated on the basis of sets reduced by various methods. ARTICLE INFO

show abstract

“…In this method we have new points instead of points with the original coordinates (Gościewski, 2013;Bauer-Marschallinger et al, 2014).…”

Section: Generationmentioning

confidence: 99%

Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

Błaszczak-Bąk¹

2016

Acta Geodynamica Et Geomaterialia

View full text Add to dashboard Cite

show abstract

“…However, the limited downlink bandwidth between satellites and ground station cannot catch up with the drastically increased data volume from high resolution spectroscopy instruments [1]. One approach to resolve this downlink bottleneck is to process the image data onboard and in real-time to decrease the data size to be transferred by downlink [2].…”

Section: Introductionmentioning

confidence: 99%

High Efficiency On-Board Hyperspectral Image Classification with Zynq SoC

Wang

Ali

et al. 2016

MATEC Web of Conferences

View full text Add to dashboard Cite

Abstract. Because of the downlink bandwidth bottleneck and power limitation on satellite, the demands for low power cost high performance on-board payload data processing which can reduce the volume of communication data are growing as well. This paper propos es a high efficiency architecture for on-board hyperspectral image classification in a Zynq Soc to achieve real-time performance. The Hamming-distance based Support vector machine (SVM) is adopted to get a high accuracy and low energy consumption for multi-class classification. The sequential control and the computing data path are realized in ARM processor and Programmable logic respectively. By the pipelined computing data path, a satisfying speedup is reached and thus lowers the energy consumption. The experiments on real hyperspectral image datasets demonstrate that our architecture can achieve 97.8% overall accuracy, 2.5~330x speed up and 11~835x energy saving compared with different state-of-art embedded platforms. For the AVIRIS spectrometer in real NASA application, it can realize real-time image classification.

show abstract

“…Very often such amount of data and uniform density is not needed, especially when the main focus is on the features of relatively flat object (e.g. : basrelief) [2]. For this purpose, it is worthwhile to use the OptD method [7].…”

Section: D Modellingmentioning

confidence: 99%

“…Generation is decreasing the dataset by creating a grid. In this method we have new points instead of points with the original coordinates [2,11]. While reduction decreases the dataset by removing some points according to the given algorithm, the remaining points are original points from the measurement [4,5,6,9].…”

Section: Introductionmentioning

confidence: 99%

The Optimum Dataset method – examples of the application

2018

View full text Add to dashboard Cite

Abstract. Data reduction is a procedure to decrease the dataset in order to make their analysis more effective and easier. Reduction of the dataset is an issue that requires proper planning, so after reduction it meets all the user's expectations. Evidently, it is better if the result is an optimal solution in terms of adopted criteria. Within reduction methods, which provide the optimal solution there is the Optimum Dataset method (OptD) proposed by Błaszczak-Bąk (2016). The paper presents the application of this method for different datasets from LiDAR and the possibility of using the method for various purposes of the study. The following reduced datasets were presented: (a) measurement of Sielska street in Olsztyn (Airbrone Laser Scanning data -ALS data), (b) measurement of the bas-relief that is on the building in Gdańsk (Terrestrial Laser Scanning data -TLS data), (c) dataset from Biebrza river measurment (TLS data).

show abstract

Optimisation of global grids for high-resolution remote sensing data

Cited by 67 publications

References 20 publications

Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

High Efficiency On-Board Hyperspectral Image Classification with Zynq SoC

The Optimum Dataset method – examples of the application

Contact Info

Product

Resources

About