Exploration of the brand new LISS4 data release and opportunities
Multispectral enlightment with NRSC, ISRO
In the exploration of LISS 4 multispectral datasets unleashed by the ISRO recently, my mindmap captures two paths. The Understanding LISS 4 path plunges into the sensor's specs against global counterparts, unveiling a product generation workflow, digging up past work done by researchers and culminating with a data exploration. Meanwhile, the Opportunities trail delves into dataset development, 5-m resolution analytics, the quest for product quality perfection and onboard computing possibilities.
Understanding LISS 4
Background
LISS 4 (aka LISS IV, LISS-IV, LISS-4) is part of the Resourcesat-2 Mission planned to provide integrated land and water resources management with enhanced multi-spectral and spatial coverage. The satellite is designed to provide both multi-spectral and panchromatic imagery of the Earth’s surface. The payload system comprises of three optical remote sensing cameras, viz., LISS-IV, LISS-III and AWiFS. LISS-IV (LISS 4) provides 5.8m resolution in three bands with 70Km swath and is the focus of this piece. The Data Users Handbook which is hosted on a German domain for some reason 😕.
The LISS 4 emerges as a royal sirji, flaunting a resolution that's considered high (or medium as per the 2024 cool kids), rocking the green, red, and near-infrared hues. In the multi-spectral mode, a 23.5 Km swath gets bathed in the three bands. Switch to mono mode, and the full 70 Km swath becomes a canvas for a solo band. It throws in a sassy tilt of ±26° in the across-track direction which makes it fashionably punctual at 5 days revisits.
Comparable global sensors
Here is a few comparable sensor specifications I compiled from the global public database:
Nice FoV, nice FWHMs, looking like a wow . Plus it is heartening to know both LISS 4 and VENµS datasets are open now.
Standard Product Generation Workflow
This is what I could interpret how the processing workflow for the generating the standard Level 2 product of LISS 4 looks like. The radiometric corrections seem to take initially care of detector-to-detector non uniformity through a look-up table made through radiometric calibration process done in a lab. This is followed by area of interest extraction using line count or ground receive time metadata. Failed or degraded detectors, which cause striping effects which can be estimated “only if the failed detectors do not exceed two consecutive CCD elements”, are then corrected. Interestingly, line loss corrections are done through averaging or repetitions depending on number of lines lost. Staggering effect, a function of tilt, speed of the platform and integration time is then sobered. Lastly, anisotropic filtering as a denoising technique is employed to a Level 1 Radiometrically corrected product.
The data is corrected for geometric distortion effects (like Earth’s curvature), band to band misregistration and ground-based fine registration to produce the Level 2 Radiometrically and Geometrically Standard product that we will be laying our hands on.
Historical work on LISS 4 products
The historical work (100+ on Google Scholar) can be categorized as:
Preprocessing
Staggering effect correction, comparison between LISS3, LISS4, AWiFS, atmospheric effects correction, coregistration, etc.
Applications
Crop classification, sugarcane classification and sugar yield prediction, mangrove classification, bacterial leaf blight identification, water quality assessment, land suitability for cashew plantation, land use classification, banana classification, seagrass detection among others.
The study that I was most excited about was “Remote sensing to map the invasive weed, Lantana camara in forest“ by Kimothi et. al. The study covers Lantana camara, an invasive weed species that has destroyed 40% of indigenous plant species coverage of the Indian tiger reserves. The study looks at an area of 3.7 km x 8 km where lantana camara field data was tagged to a fused LISS 4 and Cartosat 1 image and LISS 4 False Coloured Composite (FCC). Using 3x3 pixel windows, the authors identified statistical features to train a maximum likelihood classifier. They found that classifier trained on LISS4 FCC had a user’s accuracy of 95%, better than the fused product with Cartosat 1 at 92%. If there is more spatially diverse field data made public/available, a better classifier could be trained that can map all the lantana plantation in India. This helps us with a superb measuring tool to track efforts against probably the biggest threat to Indian forests. Geospatial community, the challenge is open!
Data Exploration
LISS 4’s FCC (NIR-R-G colour setup) can compare with high resolution images like the one used by Google Earth’s CNES/Airbus imagery (10 times better GSD). Since they maybe slightly offset in time, there are broad assessments that can be made. Here’s one comparison for an image from Bahla, Rajasthan.
More imagery can be found here.
Opportunities
Dataset Development
I would love to see who (if not ISRO) can fund development of:
subcontinental yearly RGB mosaics (with simulated blue band) for public use: Google Maps can be upto 5 years old over some areas. Plus it can be useful for many.
Cloud Optimized Geotiff datasets of yearly water body maps at LISS 4 resolution.
datasets for converting satellite imagery to basemaps for dynamic navigation basemaps.
India-wide dataset for Lantana camara shrubs.
[copy-paste any dataset idea from this list please…]
Product Quality Improvement (suggestions)
Here is a sample Colab notebook that I made with the intention to explore the dataset and find out what more can be done.
Accessible through an API
Can we please get an API that can allow us to query basis the area of interest, date-time range, cover cover threshold, product level and provides us all the products that we are looking for? It seems like the server capacity could be improved further.. one recent single product order has made me wait (“Product not online, availability will be intimated on your registered email“) for 4 hours and counting while I write this piece. I found Ujaval’s blog for accessing data (and preprocessing) manually helpful. Till then I just put a sample dataset on S3 and you can get it here as below:
# If you want to run the analysis on your data, simply replace the below path of a sample file with yours: !wget https://skyserve-nrsc.s3.us-west-2.amazonaws.com/R2F06OCT2023064684010000061SSANSTUC00GTDA.zip -qJust if there was a blue band..
There is no blue band in the LISS 4 datasets. But I found something cool in the NRSC’s User Manual page 5. Here’s the script to simulate the blue band:
band2_data = src1.read(1) band3_data = src2.read(1) band4_data = src3.read(1) # Calculate the new band data # Reference: https://www.nrsc.gov.in/sites/default/files/pdf/Software_download/UserManual.pdf new_band_data = 55.6215 + band2_data * 0.873393 - band3_data * 0.138254 - band4_data * 0.100616I think the simulated blue band RGB seems to work:
LISS4 RGB image with simulated blue band Maybe we add some more denoising?
It seems that anisotropic filter used in the preprocessing takes care of the directional noise. But I added OpenCV’s FastNLMeans filter which removes a lot of the random looking noise as seen below (with script snippet):
# Denoise the image im_rgb_denoised_nlm = cv2.fastNlMeansDenoisingColored(im_rgb_norm, None,2,2,7,21)
Striping artefact improvement
Failed or degraded detectors cause striping effects which can be estimated “only if the failed detectors do not exceed two consecutive CCD elements”. I also found striping artefacts which are one detector wide or more. For example, you can check out the striping in the green band of the LISS 4 image.
LISS 4 green band image around the location (17.3034N,78.3577E) Product ID: 2380676351 This could be tackled more generally by looking at the 2D fourier transform of the image, filtering the high-frequency stripes without modifying genuine low-frequency image features and running an inverse transform followed by a fine refinement step proposed by Lloyd et. al.. As you can see, the stripe patterns have been mostly cured. I had to run a script in my local machine for this, could not cover it in Colab notebook.
Destriped Image which shows visibly controlled striping over the input image around the location (17.3034N,78.3577E) Product ID: 2380676351 Some metadata reference would help
I noticed that NRSC has hosted the KOMPSAT-5 metadata definition under the product specifications here. I am not able to find the equivalent definitions for the LISS 4 metadata (maybe I have to dig deeper? or somebody could help?). For example, I would love to understand the definition (maybe with some illustration) of the ‘Tiltangle‘ parameter used in the metadata file of LISS 4.
Coastal georeferencing
What I have seen in some images (like Product ID: 231986861 thumbnails) is that Red and Green bands align well but NIR band can get misaligned in the coastal scenes. Possibly because of no (or few?) Ground Control Points in the sea.
Onboard Computing
OK, here is some simple math. The satellite is having a ground trace speed of 6.65 km/s according to Table 3.1 of Data Users Handbook. When the satellite is imaging in the multispectral mode, the swath is 23.5 km, which means if the sensor is operated with a setting of (23.5/6.65)=3.5 seconds between two consecutive frame captures, it is possible to cover them with no overlap. If it were possible to perform every step of the computing mentioned in the section Standard Product Generation Workflow under 3.5 seconds, it would lead to a near-realtime generation of products onboard.
As noted in Table 1 of our paper Methods to Leverage Onboard Autonomy in Remote Sensing, the interesting pixel ratio can vary from 2 to 40% depending on the use case. Thereby, a standard onboard LISS 4 product size onboard (1.8 GB uncompressed) could be brought down to upto 36 MB (2% interesting pixels) if there were a model running onboard to extract the interesting pixels within the said 3.5 seconds. The current computing systems can get you this capability if setup cleverly. A satellite-enabled continous monitoring system can thus monitor large areas in a short period of time and report the changes as they occur with spatial context. Extending this ability to satellites with multiple heterogenous sensors enables continuous monitoring of a variety of phenomena at regular intervals. Together with inter-satellite links or low power broadcast messages, a timely disemmination of actionable information to relevant stakeholders can be enabled.
If you are an operator knowing that your customers are looking for just those interesting pixels and metadata or are interested in latency of actionable information, you just might want to consider investing in the onboard computing and infrastructure to save on the downlink costs. And putting this capability on ISRO’s next Resourcesat or equivalent satellite might be a good idea too.
The open data policy is a welcome step and it seems to open doors for all to look at the high-quality data being produced.
"The best is yet to come." - Daniel Webster
Want to know more about what we are doing? Click here