Concept for an airborne real-time ISR system with multi-sensor 3D data acquisition

Haraké, Laura; Schilling, Hendrik; Blohm, Christian; Hillemann, Markus; Lenz, Andreas; Becker, Merlin; Keskin, Göksu; Middelmann, Wolfgang

doi:10.1117/12.2241051

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Due to its wingspan, the motorized glider "Stemme S10" as airborne platform offers a stable and far-reaching flight (up to 1000km). The "Stemme S10" is easy to service and can carry an additional wingpod for the real-time downlink to a ground station [10]. On a flight height between 600-750m a geometric resolution of 50 cm for the HS-camera and 1-4 pts/m² for the LiDAR sensor are achieved.…”

Section: Airborne Hyperspectral Data Acquisitionmentioning

confidence: 99%

“…Remote sensing technologies are already widely used in military context [2,8,18]. Flying platforms and their corresponding sensor-(systems) experienced a fast development during the last decade [10,13,16], mainly driven by the establishment of drone technology, active 3D point measurements and the increasing number of operating sensor channels. Passive sensors with the highest number of imaging bands are the so-called hyperspectral (HS) sensors or spectrometers.…”

Section: Introductionmentioning

confidence: 99%

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Reconnaissance of coastal areas using simulated EnMAP data in an ERDAS IMAGINE environment

Becker

Schreiner

Auer

et al. 2018

Earth Resources and Environmental Remote Sensing/Gis Applications IX

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Operations in a military naval context require a detailed planning and information gathering. For this purpose, remote sensing is a useful technique without in-situ survey. A collaboration of Fraunhofer IOSB (Institute of Optronics, System Technologies and Image Exploitation), DLR (German Aerospace Center), and Bundeswehr Geoinformation Centre ZGeoBw, engineered a desktop Geoinformation (GIS) plug-in to generate bathymetric charts and land-use-classes (vegetation, soil types and minerals) using hyperspectral data of coastal areas. These data are basis for further analyses like trafficability, barrier detection and change detection. To evaluate the potential of satellites launched in the near future with hyperspectral sensors onboard (EnMAP), aerial hyperspectral data (HySpex, AISA) were collected over a test site near the Wismar Bay in Germany and are used to simulate the satellite hyperspectral data with corresponding recording terms. Additionally, a field campaign was conducted at the Wismar Bay to acquire a ground truth dataset for model validation, including soil spectra and water depths as well. For generating the bathymetric charts, the WASI (Water Color Simulator) approach was adapted, which offers additional information besides water depth (e.g. dissolved matter, brightness of sand, relative amount of sea grass and other properties). Resulting bathymetric charts with a depth up to eight meters and unsupervised classifications of land cover are free of artefacts and accurate. A validation process is in progress. The engineered desktop GIS plug-in for HEXAGON ERDAS IMAGINE software was developed using the native SDK in addition with interoperable scripts like Python. The existing plug-in framework is variable and adaptable to different kind of GIS.

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Section: Airborne Hyperspectral Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconnaissance of coastal areas using simulated EnMAP data in an ERDAS IMAGINE environment

Becker

Schreiner

Auer

et al. 2018

Earth Resources and Environmental Remote Sensing/Gis Applications IX

Self Cite

View full text Add to dashboard Cite

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“…The availability of smaller form factors enables the development of multi sensor platforms (Haraké et al, 2016). This leads to a manifold increase in available raw data.…”

Section: Introductionmentioning

confidence: 99%

Improving Active Queries With a Local Segmentation Step and Application to Land Cover Classification

Wuttke

Middelmann

Stilla³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Active queries is an active learning method used for classification of remote sensing images. It consists of three steps: hierarchical clustering, dendrogram division, and active label selection. The goal of active learning is to reduce the needed amount of labeled data while preserving classification accuracy. We propose to apply local segmentation as a new step preceding the hierarchical clustering. We are using the SLIC (simple linear iterative clustering) algorithm for dedicated image segmentation. This incorporates spatial knowledge which leads to an increased learning rate and reduces classification error. The proposed method is applied to six different areas of the Vaihingen dataset.

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Towards Fast 3D Reconstruction of Urban Areas from Aerial Nadir Images for a Near Real-Time Remote Sensing System

Espinosa

Lenz

Gross

et al. 2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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Concept for an airborne real-time ISR system with multi-sensor 3D data acquisition

Cited by 3 publications

References 4 publications

Reconnaissance of coastal areas using simulated EnMAP data in an ERDAS IMAGINE environment

Reconnaissance of coastal areas using simulated EnMAP data in an ERDAS IMAGINE environment

Improving Active Queries With a Local Segmentation Step and Application to Land Cover Classification

Towards Fast 3D Reconstruction of Urban Areas from Aerial Nadir Images for a Near Real-Time Remote Sensing System

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