Usage
Input data format
SARXarray works with coregistered SLC/interferogram stack. SARXarray provides a reader to perform lazy loading on data stacks in different file formats, including binary format. However, we recommend to store the coregistered stack in zarr format, and directly load them as an Xarray object by xarray.open_zarr.
Loading coregistered SLC stack in binary format
If the stack is saved in binary format, it can be read by SARXarray under two pre-requisites:
- All SLCs/interferograms have the same known raster size and data type;
- All SLCs/interferograms have been resampled to the same raster grid.
For example, let's consider a case of a stack with three SLCs:
import numpy as np
list_slcs = ['data/slc_1.raw', 'data/slc_2.raw', 'data/slc_3.raw']
shape = (10018, 68656) # (azimuth, range)
dtype = np.complex64
We built a list list_slcs with the paths to the SLCs. In this case they are stored in the same directory called data. The shape of each SLC should be provided, i.e.: 10018 pixels in azimuth direction, and 68656 in range direction. The data type is numpy.complex64.
The coregistered SLC stack can be read using the from_binary function:
dtype argument since it's defaulted to numpy.complex64. The stack will be read as an xarray.Dataset object, with data variables lazily loaded as Dask Array:
<xarray.Dataset>
Dimensions: (azimuth: 10018, range: 68656, time: 3)
Coordinates:
* azimuth (azimuth) int64 0 1 2 3 4 5 ... 10013 10014 10015 10016 10017
* range (range) int64 0 1 2 3 4 5 ... 68650 68651 68652 68653 68654 68655
* time (time) int64 0 1 2
Data variables:
complex (azimuth, range, time) complex64 dask.array<chunksize=(4000, 4000, 1), meta=np.ndarray>
amplitude (azimuth, range, time) float32 dask.array<chunksize=(4000, 4000, 1), meta=np.ndarray>
phase (azimuth, range, time) float32 dask.array<chunksize=(4000, 4000, 1), meta=np.ndarray>
The loading chunk size can also be specified manually:
Reading metadata
SARXarray provides a function to read metadata from the interferogram stack coregistered by Doris v4 or Doris v5. The metadata is read as a dictionary from the slave.res file under the folder of each SLC.
Doris v4 metadata
A common Doris v4 output folder structure is as follows:
stack/
├── YYYYMMDD1/
│ ├── slc_1.res
│ ├── slc_1.raw
│ ├── ...
├── YYYYMMDD2/
│ ├── slc_2.res
│ ├── slc_2.raw
│ ├── ...
...
Where YYYYMMDD1, YYYYMMDD2, etc. are the acquisition dates of the SLCs, and slc_1.res, slc_2.res, etc. are the metadata files for each SLC.
To read the metadata from the Doris v4 stack, first build a list of the SLC metadata files:
from pathlib import Path
stack_folder = Path('stack/')
res_file_list = list(tsx_folder.glob('???????/slave.res'))
where the pattern ??????? matches the date folders. Then, you can use the read_metadata function with the driver argument set to "doris4":
Doris v5 metadata
A common Doris v5 output folder structure is as follows:
├── YYYYMMDD1/
│ ├── slc_1.res
│ ├── ifgs_1.res
│ ├── slc_1.raw
│ ├── ...
├── YYYYMMDD2/
│ ├── slc_2.res
│ ├── ifgs_2.res
│ ├── slc_2.raw
│ ├── ...
...
Where YYYYMMDD1, YYYYMMDD2, etc. are the acquisition dates of the SLCs, and slc_1.res, slc_2.res, etc. are the metadata files for each SLC. The files ifgs_1.res, ifgs_2.res, etc. are the metadata files for each interferogram, which contain the information of the sizes of the coregistered interferograms.
To read the metadata from the Doris v5 stack, first build a list of the SLC metadata files:
from pathlib import Path
stack_folder = Path('stack/')
res_file_list = list(stack_folder.glob('???????/slc_*.res'))
Then, you can use the read_metadata function with the driver argument set to "doris5":
read_metadata assumes that ifgs_*.res files are in the same folder as the slc_*.res files, and will read the interferogram sizes from them.