Application specific compression for mini-satellites with limited downlink capacity
Tobias Trenschel,Timo Bretschneider,Graham Leedham
School of Computer Engineering, Nanyang Technological University
Blk. N4 #02a-32, Nanyang Avenue, Singapore 639798
Tel: +65 – 6790 – 6045
Fax: +65 – 6792 – 6559
E-mail: astimo@ntu.edu.sg
Singapore
Abstract:
The effectiveness of a remote sensing mission is restricted by any bottl eneck in the entire system,
which comprises the actual imaging system, the satellite bus, and the ground receiving stations. One
major constraint for many mini-satellite missions is the limited downlink capacity, i.e. more data can be
acquired than transmitted. However, if the mission focuses on particular applications that do not require
the storage of the raw image data on the ground, then appropriate on-board processing can ease the
requirements on the downlink and increase the benefits and value of the satellite’s mission. One
example is hazard monitoring like fire detection with the emphasis on the location, size, and
characteristic of the fire and only secondary attention to the surrounding unaffected areas. Therefore
this paper proposes a general model for application specific compression of the imagery. The idea
comprises the aspects of image analysis with the support by an on-board database system and the
resulting compression based on the intermediate results. The software is part of a parallel processing
system, which will be flown on-board of X-Sat – Singapore’s first remote sensing satellite.
roduction
Mini-satellites face a variety of constraints that limit their downlink capacity e.g. on-board power
availability and restrictions on the a ccess to, and operation of ground receiving stations. Therefore, the
effectiveness of a remote sensing mission using a low cost mini-satellite is reduced since generally the
actual imaging system can acquire more data than can be downloaded to a ground station. Assuming
the satellite provides sufficient data storage and computational resources for a given user -defined
application, then the effectiveness of the actual mission can be improved using appropriate on- board
processing (Manduchi et al., 2000). The main idea is to move previously ground-based processing
steps on -board, and to carry out the data processing prior to transmission. The advantage is provided
by the ability to determine the locations of specific interest and thus to reduce the amount of data to be
downlinked. For example, if the application under consideration is hot-spot detection to locate fires, only
the hot-spot location needs to be transmitted if the processing can be performed on- board. Search
tasks like these can monitor a huge area without using any significant downlink bandwidth.
The overall performance of the system can be further improved by an application specific compression
scheme (Hou et al., 2000). This paper describes the creation of a compression map, which is a
generalised concept of the region -of-interest (ROI) mask of JPEG2000 (Christopoulos et al., 2000). The
compres sion map assigns continu ously adjustable weights to different regions according to their
contribution to the user- defined mission. Prior to trans mission, an image transformation, which enables
near lossless and lossy compres sion, is applied using the com puted multi-dimensional weights.
Thereby the developed technique caters for an arbitrary number of speciali sation schemes with respect
to the actual application. The transformation leads to near lossless compression for regions of high
interest – with respect to the actual application – while areas of low importance are encoded using lossy
compres sion. Consider the previously mentioned hot-spot example: the output for hot-spot detection will
be improved by orders of magnitude with respect to the limited transmission bandwidth if only the hot-spots
and their surrounding areas are transmitted. Additionally, a special but less significant interest in
urban areas and reservoirs enables evacuation and provision of water to counteract the fires. All this
can be accomplished with little further costs involved since only a relatively small amount of data has to
be transmitted to provide all the required infor mation. To fulfil the required task of detecting ROIs the.proposed system utilises un supervised classification, determines the so- called compression map
according to the user’s application, and applies the gained information to compress the raw image. Note
that all those processing steps are carried out according to the specifications provided by the user.
For evaluation of the technique an investigation compares compression assuming homogeneous
interest, like it is generally sup ported, and variable content-based compression according to a compres -sion
map, reflecting the ap plication. An analysis investigates the gain of the proposed technique and
introduces an application specific error measure. In summary the analysis proved that a significant im-provement
in the bandwidth usage is achievable for specialised applications. The model was developed
for the small satellite X-Sat, which is designed and built by Nanyang Technological University.
This paper is organised as follows: Section 2 provides an introduction to X-Sat and its computa tional re-sources.
Section 3 provides an overview of the content-based compression model and describes the
different processing steps in detail. The actual results and their discussion are presented in Section 4
while Section 5 summaries the paper.
Overview of X-Sat and its on-board processing facilities
X- Sat is a small-satellite with a mass of approximately 120 kg. It carries three payloads, namely the
imaging sys tem IRIS, the buoy detection instrument ADAM, and the parallel processing unit (PPU).
Henceforth only the imaging system and the PPU will be considered since the emphasis of this paper is
on image processing with the aim to reduce the constraining impact of the downlink. The camera is a
push- broom scanner with three individual scan lines in the green (520 nm – 600 nm), red (630 nm –
690 nm), and near-infrared (760 nm – 890 nm) wavelength range. The spatial resolution is specified to
be 10 m for a mean orbit altitude of 685 km. The main bottleneck is the downlink of the imagery since
the only available ground station is located in Singapo re. In addition to the relative short visibility of the
satellite in the range of the receiving antenna the mission objective of imaging over Singapore and the
surrounding areas collides with the transmission. Due to power restrictions both operations cannot be
run simultaneously. Therefore image compression is required to increase the data and information
throughput with respect to the given bandwidth. This is enabled by the parallel computer payload. The
PPU con sists of four fully connected radiation-hardened field programmable gate arrays (FPGAs) each
hosting five processing nodes. Only four of the nodes associated with each FPGA block are operational
at any moment in time. The fifth node is a spare and will only be employed if one of the other four nodes
becomes inoperative, e.g. through radiation. The individual nodes comprise of a StrongArm processor
and 64 MByte of local memory. The resulting architecture is a mesh with wrap- around and therefore
perfectly suited for image processing tasks. For the storage of the acquisition data a 2 GByte RAM disk
is attached to the PPU.
