Concept and integration of an on-line quasi-operational airborne hyperspectral remote sensing system

Abstract

Modern mission characteristics require the use of advanced imaging sensors in reconnaissance. In particular, high spatial and high spectral resolution imaging provides promising data for many tasks such as classification and detecting objects of military relevance, such as camouflaged units or improvised explosive devices (IEDs). Especially in asymmetric warfare with highly mobile forces, intelligence, surveillance and reconnaissance (ISR) needs to be available close to real-time. This demands the use of unmanned aerial vehicles (UAVs) in combination with downlink capability. The system described in this contribution is integrated in a wing pod for ease of installation and calibration. It is designed for the real-time acquisition and analysis of hyperspectral data. The main component is a Specim AISA Eagle II hyperspectral sensor, covering the visible and near-infrared (VNIR) spectral range with a spectral resolution up to 1.2 nm and 1024 pixel across track, leading to a ground sampling distance below 1 m at typical altitudes. The push broom characteristic of the hyperspectral sensor demands an inertial navigation system (INS) for rectification and georeferencing of the image data. Additional sensors are a high resolution RGB (HR-RGB) frame camera and a thermal imaging camera. For on-line application, the data is preselected, compressed and transmitted to the ground control station (GCS) by an existing system in a second wing pod. The final result after data processing in the GCS is a hyperspectral orthorectified GeoTIFF, which is filed in the ERDAS APOLLO geographical information system. APOLLO allows remote access to the data and offers web-based analysis tools. The system is quasi-operational and was successfully tested in May 2013 in Bremerhaven, Germany

Evaluation and performance analysis of hydrocarbon detection methods using hyperspectral data

“…Also, the detection of hydrocarbons on water bodies and vegetation is yet to be further investigated. The methods are to be integrated in a technology demonstrator, like Fraunhofer IOSB's hyperspectral multisensory platform for fast image acquisition and near-realtime data processing [9]. Such a system may be adapted for projects in environmental monitoring, disaster management, pipeline monitoring and hazard mapping.…”

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

“…Apollo provides spatial information infrastructure construction and geographic information sharing services, which can provide users with image data management, publishing, sharing and services. Apollo's spatial data architecture provides an interactive framework, supports the management and service of metadata and maps, and provides a secondary development package, which can build a spatial information system according to the application requirements and simplify the complex business workflow [104].…”

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

“…Because operability is a major criterion in military context, Fraunhofer IOSB developed a ready to fly sensor wingpod [17]. The set of sensors installed includes a hyperspectral camera (aisaEagle: 390-990nm, 128 bands, aisaHawk: 99-2500nm, 239 bands), a LiDAR system (Riegl VQ-580) and an IMU/INS sensor (Applanix) to record permanently the exact position and orientation during the flight ( Figure 2).…”

“…Passive sensors with the highest number of imaging bands are the so-called hyperspectral (HS) sensors or spectrometers. Their airborne installation, image acquisition and analysis is already state of the art [8,11,12,17] and the data offers ground resolution in a centimeter scale. Since hyperspectral sensors are becoming smaller, their usage on drones is just upcoming [1,4].…”