The document discusses small-scale satellites, highlighting their benefits in remote sensing, such as low cost and frequent image capture, along with their limitations. It outlines the co-registration process for satellite data, particularly for the Diwata and Hodoyoshi-1 satellites, while providing guidelines for data access, usage, and analysis. Additionally, it includes technical details on satellite specifications and instructions for software installation to facilitate image processing.

1. Center for Research and Application for Satellite Remote Sensing
Yamaguchi University
Co-Registration of Small-Scale
Satellite Data

2. Introduction to small-scale satellites
Small-scale satellites
A small-scale satellites is a small satellite typically with a mass around 50 kilograms. It has a
shorter life cycle (few years), and low cost. These small-scale satellites may offer less
coverage of the earth at lower or medium spatial resolution, but they provides more
frequent image capture at a much lower cost. Thus, if we send a large number of small-scale
satellites into orbit, they will increase coverage of the Earth.
In 2014, more than ten small-scale satellites for remote sensing will make constellation, and
they will contribute to monitoring earth environment, such as natural resource management,
human settlements, environment conservation, natural disaster management.

3. Microsatellites have great potential as remote sensing platforms because of the cost-
effective implementation of satellite constellations and formations, which increase their
overall temporal resolution and ground coverage. However, microsatellites have many
limitations, such as:
1. Geometric distortion due to alignment, band to band registration, satellite orbit and
attitude, and projection conversation algorithm.
2. Shift between bands.
Limitations in use of small-satellites

4. Geometric distortion of Diwata-1 microsatellite image (left: Diwata-1 image with false color composite, right: ESRI world imagery map)

5. Co-registration of Diwata satellite data to
Landsat data

6. Sensor HPT SMI WFC
FOV 1.9 x 1.4 km 52 x 39 km 180° x 134°
Spatial resolution 3 m 80 m 7 km
Spectral range NIR, R, G, B 420-650 nm
700-1050 nm
Panchromatic
Spectral resolution 10-20 nm
Sensor specifications of Diwata-1

7. A guide to using satellite/microsatellite data
Diwata
1. Go to website: https://data.phl-microsat.upd.edu.ph/login and sign in account. If you don’t have an
account, create an account at “Sign up”(a).
2. For case 1, select “Diwata1” under Satellite. In the Date of Acquisition, select “Custom” and define period
in 1 - 30 April 2018 (b).
a
b

8. A guide to using satellite/microsatellite data
Diwata
3. Download image in 2018-04-19 (band NIR) by click the image and press
“download image” icon (c).
4. Download image in 2018-04-19 (band Red) (d).
c
d

9. Diwata
5. For case 2, select “Diwata2” under Satellite. In the Date of Acquisition, select “Custom” and
define period in 27 May - 6 July 2019 (e). Download image in 2019-06-22 (band NIR) (f).
6. In the Date of Acquisition, select “Custom” and define period in 1 - 30 June 2019 (g). Download
image in 2019-06-22 (band Red) (h).
e
f
g
h
A guide to using satellite/microsatellite data

10. Landsat
1. Go to website: https://earthexplorer.usgs.gov/ and log in an account. If you don’t have
an account, please “Register” (a).
2. Select “Data Sets” tab (b), go to “Landsat”and “Landsat Collection 1 Level-1”, then
thick mark at “Landsat 8 OLI/TIRS C1 Level-1” (c) .
a
b
c
A guide to using satellite/microsatellite data

11. Landsat
3. Select “Additional Criteria” tab (d)
4. For case 1, use “LC08_L1TP_116050_20180306_20180319_01_T1” by type in “Landsat Product Identifier” box (e),
and then press “Results” button below.
5. The data will show in “Results” tab (f).
6. The satellite image can show on basemap by press “Show Browse Overlay” icon (g).
d
e
f
g
A guide to using satellite/microsatellite data

12. Landsat
7. Download the data by press “Download Options” icon (h).
8. Click “Download” button for Level-1 GeoTIFF Data Product in “Download Options” window (i).
9. Follow 3. - 8. for download data in case 2, but use
“LC08_L1TP_125050_20190425_20190508_01_T1”
by type in “Landsat Product Identifier” box (e).
h i
A guide to using satellite/microsatellite data

13. A guide to using satellite/microsatellite data
Installation of Anaconda
● Download the Anaconda installer from https://www.anaconda.com/products/individual and select the
operating systems.
● Then double click the installer to launch, Read the licensing terms and click “I Agree”.
● Select an install for “Just Me” unless you’re installing for all users (which requires Windows Administrator
privileges) and click Next.

14. conda create --name arosics
conda activate arosics
conda install -c conda-forge python numpy gdal scikit-image matplotlib
pyproj "shapely<=1.6.4" geopandas pandas cmocean
conda install -c conda-forge pyfftw
pip install arosics
python [installation path of Anaconda]envsarosicsScriptsarosics_cli.py
Installation of AROSICS and prerequisites
• Open Anaconda prompt
A guide to using satellite/microsatellite data

15. Layer stacking the Diwata data
Example:
● Stack output: The result name “XXXX.tif”
● NIR of Diwata:
[Path of Diwata NIR]D1_SMI_2018-04-19T073107096_N840.tif
● Red band of Diwata:
[Path of Diwata Red band]D1_SMI_2018-04-19T073107046_V670.tif
gdal_merge.py -o [stack output] -separate [NIR of Diwata] [Red band of
Diwata
A guide to using satellite/microsatellite data

16. Example:
● Reference Landsat:
[Path of Landsat
image]LC08_L1TP_116050_20180306_20180319_01_T1_B5.TIF
● Target Diwata image to be shifted:
[Path of Diwata image to be shifted]XXXX.tif
Coregistration by AROSICS
python [installation path of Anaconda]envsarosicsScriptsarosics_
cli.py local -nodata 0 0 -ws 1024 1024 -max_shift 300 -max_points 2000
-bs 1 -min_reliability 20 [reference Landsat] [target Diwata image to
be shifted] 1
A guide to using satellite/microsatellite data

17. NOTE: In case of [error message indicating different projection], it should be transform coordinate reference
system of the data by the command below.
gdalwarp -t_srs [Target CRS] -r cubic [input Diwata] [output transformed]
Example:
● Target CRS for Landsat data, such as EPSG:32648
● Input diwata:
[Path of input Diwata]D1_SMI_2018-04-19T073107096_N840_L1C.tif
● Output transformed:
[Path to collect output transformed]D1_SMI_2018-04-
19T073107096_N840_L1C.transformed.tif
A guide to using satellite/microsatellite data

18. Example: Error message indicating different projection.
Traceback (most recent call last):
File "C:UsersepinurseAnaconda3envsarosicsScriptsarosics_cli.py", line 362, in <module>
parsed_args.func(parsed_args)
File "C:UsersepinurseAnaconda3envsarosicsScriptsarosics_cli.py", line 73, in run_local_coreg
CRL = COREG_LOCAL(args.path_ref,
File "C:UsersepinurseAnaconda3envsarosicslibsite-packagesarosicsCoReg_local.py", line 235, in __init__
self.COREG_obj = COREG(self.imref, self.im2shift,
File "C:UsersepinurseAnaconda3envsarosicslibsite-packagesarosicsCoReg.py", line 310, in __init__
self._get_image_params()
File "C:UsersepinurseAnaconda3envsarosicslibsite-packagesarosicsCoReg.py", line 446, in
_get_image_params
raise RuntimeError(
RuntimeError: Input projections are not equal. Different projections are currently not supported. Got +proj=utm
+zone=48 +datum=WGS84 +units=m +no_defs / +proj=longlat +datum=WGS84 +no_defs.
A guide to using satellite/microsatellite data

19. The river from band NIR of Diwata 1 satellite image.
The river from band NIR of Landsat 8 satellite image. The river from band NIR of Landsat 8 satellite image.
The river from Red band of Diwata 1 satellite image.
The original data (band Red and NIR) overlaid on Landsat

20. The river from shifted data of Diwata 1
satellite image.
The river from band NIR of Landsat 8
satellite image.
The shifted data overlaid on Landsat.

21. Co-registration of Hodoyoshi-1 satellite
data to ASTER data

22. Satellite and sensor Hodoyoshi-1 / Optical sensor
Spatial resolution 6.7m
Swath width 27.8km
Wavelength 0.45-0.52µm
0.52-0.60µm
0.63-0.69µm
0.78-0.89µm
Bit depth 12 bits
Data compression JPEG2000
Sensor specifications of Hodoyoshi-1

23. 1. Follow the steps of installing Anaconda and AROSICS
2. Install Bash on Anaconda by the command below in Anaconda Prompt
conda install -c anaconda bash
3. Acquire codes from GitHub by the command below in Anaconda Prompt
git clone https://github.com/heromiya/coregistration_aster.git
4. Locate a sample data set in “coregistration_aster” from
https://drive.google.com/file/d/17qilvOzS1esms9Hmz8fpyr_3mSFaUwqk/view?usp=sharing
5. Run the script by the command below in Anaconda Prompt
bash shift.sh
Steps to run the co-registration

24. Result - Reference ASTER image (NIR)

25. Result - Shifted Hodoyoshi-1 image (NIR)

26. 1. Change detection using NDVI
a. Calculate NDVI for the co-registered data and reference
data
b. Calc difference of NDVI between co-registered data and
reference data
c. Visualize by Choropleth map with a classification using
μ±2σ.
i. Pixel value < μ - 2σ → negative change
ii. μ - 2σ < pixel value < μ + 2σ → no change
iii. μ + 2σ < pixel value → positive change
A possible application of change detection using the co-registered data