# IRIS service specifications

## Service Description

The Change Detection Analysis (IRIS) is a processing service developed by NHAZCA S.r.l. that implements image-processing algorithms for the monitoring of ground and structural changes.

The Change Detection Analysis is conceived to work with couples of optical images, as it calculates the changes occurred in the secondary image with respect to the reference one. The processor will automatically crop the images on the maximum common area and, if needed, resample one of the two images to match the Ground Sample Distance (GSD) of the other.

A co-registration step is then applied to ensure the alignment of the two images with as much precision as possible, this is achieved employing a full field displacement measurement based on the Dense Inverse Search method for Optical Flow1, which results are used to estimate an affine transformation of the secondary image minimizing the residual shift.

The final product is a Structural Similarity Index Measure (SSI or SSIM) map2, that graphically and numerically displays the occurred changes.

The Structural Similarity Index is defined as:

$SSIM(x,y) = \frac {(2 μ_x μ_y + C_1) \times (2 σ_{xy} + C_2)} {(μ^2_x + μ^2_y + C_1) \times (σ^2_x + σ^2_y + C_2)}$

where: $$μ_x$$ ($$μ_y$$), $$σ_x$$ ($$σ_y$$) and $$σ_{xy}$$ are, respectively, the average (weighted with a Gaussian filter), standard deviation and cross-covariance for x (y) image patch, C1 and C2 are variables which depends on the dynamic range of the pixels.

The SSIM is computed on a sliding window (patch) of fixed size and the value is assigned to the central pixel of the window. The results consist of numerical maps with a value in the range [0 – 1] assigned to each pixel of the region of interest, where 1 is the maximum SSIM (representing a perfect similarity between images) and 0 is the minimum SSIM value (representing the maximum observable change). The change maps are then converted in a coloured overlay map which can be superimposed to the original image to create an easily and immediately understandable product in a range of 0 (no changes) – 1 (maximum change). The images obtained in this way are three-band (RGB) geotiff that can be imported in any GIS environment.

Note

This service supports only Optical EO data.

Warning

IRIS supports only Calibrated Datasets from the following EO mission: Landsat-8, PlanetScope, Pleiades-1, Sentinel-2, and WorldView.

## Inputs

The service supports EO optical Data and can analyse every band of the images. Input of the IRIS service is a couple of reflectance images from Calibrated Dataset [CD] obtained from the Optical Calibration processor. This image pair shall be made of:

1. a pre-event acquisition,

2. and a post-event acquisition.

Warning

The pair of input Geophysical Assets must be from the same sensor and shall have the same CBN (e.g. red for both pre- and post-event image).

Note

To get better results from the Change Detection Analysis service it is better to use a co-registered pair having the same GSD.

## Parameters

The IRIS service requires mandatory and optional parameters. All service parameters are listed in the below Table 1.

Parameter Description Required Default value
Optical calibrated pre-event single band asset Reference single-band geophysical asset used in the change detection analysis YES
Optical calibrated post-event single band asset Secondary single-band geophysical asset used in the change detection analysis YES
Window size Extension of the sliding window used to perform the change detection YES
Area of Interest A polygon representing the area of interest to be analysed in WKT format NO

Table 1 - Service parameters for the IRIS processor.

### Pre and post event geophysical asset

The first two mandatory parameters define the input "Reference" and "Secondary" images from Optical Calibrated Datasets. Input for Optical calibrated pre-event single band asset and Optical calibrated post-event single band asset parameters are the path to the single-band geophysical assets from the two Calibrated Datasets. This parameter is required to specify both the reference to the Calibrated Dataset and the band (specified as CBN) to use for the analysis (e.g. red, or green, etc).

In the definition of the input geophysical single band asset the drag and drop of the single-band asset is foreseen. This is possible by dragging and dropping one of the single-band assets (CBNs) included into a Calibrated Dataset retrieved from the features in the Results panel or in the feature basket.

Hint

To consult the bands of a Calibrated Dataset, click on the Show assets button available near the feature title. A list with all single-band assets (CBNs) included within the Calibrated Dataset will appear under the feature title.

The drag and drop of the geophysical asset provides the input dataset reference to the service in the format:

input_dataset_reference#single-band_asset

Note

This string format is the only type of input accepted.

As an example, after the drag and drop of a feature the following string will be automatically inserted as a value for the parameter:

https://catalog.charter.uat.esaportal.eu:443//charter/cat/[chartercalibrateddataset,%7Bcallid10[…]C08_L1TP_188034_20210218_20210304_01_T1-calibrated#blue"

Warning

Users must drag and drop the single-band asset (e.g. "red") into both Optical calibrated pre-event single band asset and Optical calibrated post-event single band asset fields. The drag and drop of the Calibrated Dataset (e.g. "[CD] SENTINEL-2A MSI L2A 46 2021/12/11 02:31:11") is not enough.

### Window size

The user must provide a value for the window size, which defines the size of the sliding window in pixels, this parameter can highly influence the result of the analysis. The higher this parameter is set, the more averaged the change map will be, while the smaller and the more detailed changes can be identified at the cost of a potentially noisier results. This is due to the SSIM value for each pixel being computed using the information present in the whole sliding window, thus obtaining a more localized value of the index in case of a smaller window. As a rule of thumb, the dimension of the window should be set in a range between 9 and 71. This range depends on the type of changes the user wants to identify. The output SSIM maps will have the same Ground Sample Distance of the selected band.

Warning

Window size shall be in a range between 9 and 71. The inserted value must be odd.

### AOI (optional)

This last parameter (optional) may define the area of interest expressed as a Well-Known Text value.

Tip

In the definition of “Area of interest as Well Known Text” it is possible to apply as AOI the drawn polygon defined with the area filter. To do so, click on the :fontawesome-solid-magic: button in the left side of the "Area of interest expressed as Well-known text" box and select the option AOI from the list. The platform will automatically fill the parameter value with the rectangular bounding box taken from the current search area in WKT format.

## Outputs

The IRIS processor provides as output the following products:

1. The pair of single-band reflectance assets defined as input

2. SSIM change detection overview

3. SSIM change detection values

The first output product of this service is a georeferenced single band image containing the value of the SSIM assigned to each pixel of the region of interest with values between 0 (maximum observable change) and 1 (perfect similarity between images). The second one represents a georeferenced RGB representation of the SSIM map with an associated colorbar legend.

Warning

Output single-band reflectance products are the inputs assets and not the co-registered ones derived within the IRIS processing.

IRIS Product Specifications can be found in the below tables.

Attribute Value / description
Long Name SSIM change detection overview
Short Name overview-iris
Description RGB colour composite representing the SSIM map
Data Type Unsigned 8-bit Integer
Band 3
Format COG
Projection As per the input images
Valid Range [1 - 255]
Fill Value NaN
Attribute Value / description
Long Name SSIM change detection values
Short Name change-detection-raw
Description SSIM value assigned to every pixel of the image
Data Type Float 32
Band 1
Format COG
Projection As per the input images
Valid Range [0 - 1]
Fill Value NaN

1. Kroeger, T., Timofte, R., Dai, D., Van Gool, L. (2016), "Fast Optical Flow Using Dense Inverse Search", In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision – ECCV 2016. Lecture Notes in Computer Science, vol 9908. Springer, Cham. DOI: 10.1007/978-3-319-46493-0_29

2. Zhou, W., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, (2004) "Image Quality Assessment: From Error Visibility to Structural Similarity." IEEE Transactions on Image Processing. Vol. 13, Issue 4, April 2004, pp. 600–612. DOI: 10.1109/TIP.2003.819861