record the data to files, process them by rea the files and writing them back on to the
disk.Therefore they consume a lot of resources and are deemed to be sub-optimal
applications that demand quick response.
Figure 1. Ground System Processing to ge Product for IRS Cartosat-1
In the recent past, the computing paradigm shift from usual sequential processing to
architectures duetothe clock speeds hitting th the major processor vendors manufacture wi
in a single CPU. GPGPU General Processing Unit is the latest addition in the
Computing HPC domain. GPU is an acce contains very large number of cores, speci
perform Single Instruction Multiple Data operations. Similarly, Field Programmable G
are special electronic chips containing larg and Common Logic Blocks CLB which can
behave like an electronic circuit, thus can performance by consuming less powe
Processors DSPs which are inherently designed for specific processesand perform
fast.
On the other hand there has also been a sig standardization of interfaces and progr
Interfaces. PCIe[7] has emerged as de-facto of the add-on accelerator cards, FPGA c
cards. OpenCL[8] and OpenMP[9] have em programming
interface which
support architectures and are vendor neutral. All t
have given system engineers greater flexi right platform for a given task and integrate
system, single instance of OS and a single pr
Heterogeneous computing or sometimes a Hybrid Computing [10] platforms integrate
platforms such as CPUs, FPGAs and GPUs which can perform complex tasksworkflo
achieve higher throughput.
[11]demonstrated the usage of Metacomp multiple resources and high speed netw
processing and visualization of existing remo the process does not address the real-time pa
discusses a requirement of storing the real- real-time database using file buffers. It
reading the data from the files on the hard
of time, computing al and ineffective for
generate Level-1A 12
m has taken a radical to parallelmulti-core
g the limits. Today, all with around ten cores
l Purpose Graphics the High Performance
ccelerator card which ecifically designed to
ata SIMD type of e Gate Arrays FPGA
arge number of Gates can be programmed to
an achieve very high wer. Digital Signal
tly parallel and are orm these tasks very
significant progress in ogramming language
cto interface for many cards, and GPGPU
emerged as an ideal orts
all multi-core
ll these developments exibility to chose the
ate them into a single program.
s also referred to as ate various computing
s into a single system flows in parallel and
mputing by utilizing etwork for real-time
mote sensing data, but pass acquisition. [12]
-time data into non- It does not discuss
elements of payload data processin use of Heterogeneous Computing u
high throughput for different doma This paper discusses a novel
processing Remote Sensing Data being received using a heterog
namely XSTREAM having a comb FPGA.
Section 2 discusses the methodol aspects, mapping of the tasks to th
synchronization of all the modules products and display them in orig
while the pass is being acquired discusses the test results and comp
data processing chain in terms of qu 4 discusses the conclusion and futu
2. METHOD
2.1 Level-1A product generation
Figure 1 depicts the sequence of generation of level-1A product als
for any analysis from raw signal d nature of the problem is listed be
staggered placement of the CCD odd and even pixels are recorded, p
and sent as I Q channels. Hence to be performed for two streams
ixand finally combined to gener x to step xiii.
i. Detection of Valid Frame
search for frame header i. valid frames.
ii. Time Stamping:
Time stamp Reception Time GRT, wh
Translator TCT iii.
Aux Separation: Separate th
data and process aux data an iv.
Decode: Decoding is done,
encoding.In Cartosatmissio Solomonencoding method
byte errors in 247 bytes. v.
Decrypt: Decrypt the dat
methodology used in the used is stream cipher and th
of this paper. vi.
Decompress: Most of the
use lossy compression tec downlink rates vis-a-vis d
compressions are data dep length bit stream after c
JPEG2000 etc. In Car compression,augmented wi
and Huffman coding is u searching of header, follo
stream, de-quantization and
vii. Aux Processing:
The star board time etc also know
used to construct orbit and with respect to the imagin
computing the geo-location known as latlongs and tag
computed latlongs. sing. [13][14] demonstrated the
g using GPGPUs for achieving mains.
approach of acquiring and ta in real-time as the data is
ogeneous computing platform mbination of CPUs, GPUs and
dology adopted, re-engineering the right computing device and
lesin order to generate the data original resolution in real time
ired and processed. Section 3 mparison with the conventional
f quality and accuracies. Section uture directions.
DOLOGY ion process:
of steps to be performed for also referred to as basic product
l data and a brief description of below. Please note that due to
D arrays in Cartosat missions, d, processed in separate streams
ce the following processes need ms separately step i to step
nerate a level-1A product step mes:
Read the bit stream and r i.e., FSC code and store the
mp each frame with the Ground which is read from Time Code
the Aux data from the video a and video data independently.
e, which is a reverse process of sions standard 255247 Reed-
is used, which can correct 4- data based on the encryption
he mission. The methodology the details are out of the scope
e high resolution RS satellites techniques to match the data
data acquisition rates. These ependent and produce variable
compression JPEG, DPCM, Cartosat series,JPEG[16] like
with a rate control algorithm s used. This process involves
ollowed by decoding the bit- nd inverse transformation.
ar sensor data, gyro data, on- own as or ephemeris data are
nd attitude information OAT ging time. These are used in
ion latitude and longitude also tagging the image line with the
ISPRS Technical Commission VIII Symposium, 09 – 12 December 2014, Hyderabad, India
This contribution has been peer-reviewed. doi:10.5194isprsarchives-XL-8-1171-2014
1172
viii. Stagger Estimation:
Stagger value is specific to missions having staggered placement of CCD arrays like Cartosat
satellites. The stagger value depends on the satellite look direction Roll bias, and hence it changes for each
scene. This requires complex geo-processing using OAT values.
ix. Radiometric normalization:
Due to the non-linear nature of CCD devices, a gray level normalization process is to
be performed. Often this process involves passing through a LookUp Table LUT for every pixel. LUT is
pre-computed table based on lab settings, and is also routinely updated using a calibration test site.
For steps i-ix, the data is processed in two independent streams I Q in parallel, which have odd and even images
separately without attaching the geo co-ordinates to the data. Further processeslisted through steps x-xiii are performed
to combine and align the data to generate the basic level-1A product.
x.
IQ channel alignment : Since the data is processed
from two independent chains there is a possibility of data mis-alignment due to any reasons, such as, loss of
frames, bit-errors,process exception in one of the channels IQ etc. Hence the data from both I Q
channels are to be aligned using the time tag and line count extractedfrom the video data.
xi. Stagger Correction:
As explained in step-viii, due to staggered placement of odd pixels and even pixels in the
focal plane one of the images need to be shifted resample by a value computed in step viii to generate
a stagger freefull swath original resolution image.
xii. Strip Separation and Geo Tagging:
For missions like Cartosat-2 which involves manoeuvring between two
successive spot acquisitions the data may contain manoeuvre data during of RT acquisition and in case of
PB the strips are streamed continuously without any manoeuvre data. Hence the process involves separating
the scenes and writing the separatedstrips as independent images. Aux processed values OAT is processed and
stored as Auxiliary Data Interchange Format ADIF and are used to compute the geo-coordinates of the scene.
The Geo-coordinatesat regular intervals and are stored in a separate file grd file and is used in subsequent
processing, such as level-2 processing andlatlong readout in display process etc.
xiii. Geo-Image generation:
The image thus generated in step xi and geo coordinates computed at regular intervals in
step xii are combined together and written in a native format as a level-1A product the design is flexible and
can be written in any other open format like HDF5[15].
2.2 Analysis Design:
