Within the DLR project COMPASSO, optical clock and link technologies will be evaluated in space on the Bartolomeo platform attached to the Columbus module of the ISS. The system utilizes two iodine-based frequency references, a frequency comb, an optical laser communication and ranging terminal and a GNSS disciplined microwave reference. While COMPASSO is specifically dedicated to test optical technologies relevant for future satellite navigation (i.e. Galileo), the technologies are also crucial for future missions related to Earth observation and science. The optical frequency reference is based on modulation transfer spectroscopy (MTS) of molecular iodine near a wavelength of 532 nm. An extended cavity diode laser (ECDL) at a wavelength of 1064 nm is used as light source, together with fiber-optical components for beam preparation and manipulation. The laser light is frequency-doubled and sent to a mechanically and thermally highly stable free-beam spectroscopy board which includes a 20 cm long iodine cell in four-pass configuration. The iodine reference development is lead by the DLR-Institute of Quantum Technologies and includes further DLR institutes, space industry and research institutions. Phase B of the project will be finalized soon and an Engineering Model of the iodine reference, which represents the flight models in form, fit and function, will be realized by mid 2023. The launch of the COMPASSO payload is planned for 2025. Additional presentation content can be accessed on the supplemental content page.
The general trend in remote sensing is on one hand to increase the number of spectral bands and the geometric resolution of the imaging sensors which leads to higher data rates and data volumes. On the other hand the user is often only interested in special information of the received sensor data and not in the whole data mass. Concerning these two tendencies a main part of the signal pre-processing can already be done for special users and tasks on-board a satellite. For the BIRD (Bispectral InfraRed Detection) mission a new approach of an on-board data processing is made. The main goal of the BIRD mission is the fire recognition and the detection of hot spots. This paper describes the technical solution and the first results, of an on-board image data processing system based on the sensor system on two new IR-Sensors and the stereo line scanner WAOSS (Wide-Angle-Optoelectronic-Scanner). The aim of this data processing system is to reduce the data stream from the satellite due to generations of thematic maps. This reduction will be made by a multispectral classification. For this classification a special hardware based on the neural network processor NI1000 was designed. This hardware is integrated in the payload data handling system of the satellite.
The DLR small satellite BIRD (Bi- spectral Infrared Detection) is successfully operating in space since October 2001. The main payload is dedicated to the observation of high temperature events and consists mainly of a Bi-Spectral Infrared Push Broom Scanner (3.4-4.2μm and 8.5-9.3μm), a Push Broom Imager for the Visible and Near Infrared and a neural network classification signal processor.
The BIRD mission answers topical technological and scientific questions related to the operation of a compact infra-red push-broom sensor on board of a micro satellite. A powerful Payload Data Handling System (PDH) is responsible for all payload real time operation, control and on-board science data handling. The IR cameras are equipped with an advanced real time data processing allowing an autonomously adaptation of the dynamic range to different scenarios. The BIRD mission control, the data reception and the data processing is conducted by the DLR ground stations in Weilheim and Neustrelitz (Germany) and is experimentally performed by a low cost ground station implemented at DLR Berlin-Adlershof. The BIRD on ground data processing chain delivers radiometric and geometric corrected data products, which will be also described in this paper. The BIRD mission is an exemplary demonstrator for small satellite projects dedicated to the hazard detection and monitoring.
The general trend in remote sensing is on one hand to increase the number of spectral bands and the geometric resolution of the imaging sensors which leads to higher data rates and data volumes. On the other hand the user is often only interested in special information of the received sensor data and not in the whole data mass. Concerning these two tendencies a main part of the signal pre-processing can already be done for special users and tasks on-board a satellite. For the BIRD (Bispectral InfraRed Detection) mission a new approach of an on-board data processing is made. The main goal of the BIRD mission is the fire recognition and the detection of hot spots. This paper describes the technical solution, of an on-board image data processing system based on the sensor system on two new IR- Sensors and the stereo line scanner WAOSS (Wide-Angle- Optoelectronic-Scanner). The aim of this data processing system is to reduce the data stream from the satellite due to generations of geo-coded thematic maps. This reduction will be made by a multispectral classification. For this classification a special hardware based on the neural network processor NI1000 was designed. This hardware is integrated in the payload data handling system of the satellite.
The general trend in remote sensing is on one hand to increase the number of spectral bands and the geometric resolution of the imaging sensors which leads to higher data rates and data volumes. On the other hand the user is often only interested in special information of the received sensor data and not in the whole data mass. Concerning these two tendencies a main part of the signal pre-processing can already be done for special users and tasks on-board a satellite.
For the BIRD (Bispectral InfraRed Detection) mission a new approach of an on-board data processing is made. The main goal of the BIRD mission is the fire recognition and the detection of hot spots.
This paper describes the technical solution, of an on-board image data processing system based on the sensor system on two new IR-Sensors and the stereo line scanner WAOSS (Wide-Angle-Optoelectronic-Scanner). The aim of this data processing system is to reduce the data stream from the satellite due to generations of geo-coded thematic maps. This reduction will be made by a multispectral classification. For this classification a special hardware based on the neural network processor NI1000 was designed. This hardware is integrated in the payload data handling system of the satellite.
A neural method for gray value segmentation now is applied to texture segmentation. The parallel-sequential algorithm is based on recursive nonlinear feature smoothing in a 4- neighborhood. The smoothed feature values then can be segmented using an adaptive adjacency criterion which defines a special graph structure, called the Feature Similarity Graph. The segments are the connected components of that graph. The combination of results from the different image features is done in a hierarchical process starting, like in the human visual system with gray value segmentation. Besides segments with homogeneous gray value this process also provides texture elements which are the basis for the calculation of new image features. Then, first, the modulus of the gray value gradient is used as a new feature of the original image. The following segmentation basing on that feature provides regions which are homogeneous with respect to the mean gray value gradient. Furthermore, texel directions are calculated. That feature contains information on texture orientation of textured image regions. With these features the same neural segmentation method is able to separate not only regions with different mean gray values but also those with different textures.
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