Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada

Figures & data

Figure 1. Coronation Gulf marine region (red polygon), Nunavut, Canada. The image is a RSAT-2 dual-polarized (RGB: HH-HH-HV) ScanSAR Wide image acquired 2010-04-26 00:42:04 UTC.

Figure 2. Example of a CIS weekly ice chart of Canadian Western Arctic. Full ice coverage (10 tenths) is shown in (a) (October 18, 2010) and ice free (0 tenths) is shown in (b) (August 16, 2010). Red rectangles highlight the Coronation Gulf marine region.

Table 1. Sampling periods for full ice coverage (10/10 landfast ice) and ice-free conditions.

Year	Ice-free		Full-ice Coverage
	Start	End	Start	End
2009	–	–	31/12	28/06
2010	23/08	17/10	20/12	13/06
2011	25/07	25/10	19/12	16/05
2012	26/07	23/10	27/11	12/05
2013	23/07	28/10	10/12	20/05
2014	12/08	27/10	02/12	17/05
2015	03/08	13/10	03/12	17/05
2016	27/07	11/10	07/12	11/05
2017	18/07	23/10	24/12	07/05
2018	18/07	16/10	29/11	11/06
2019	13/08	24/09	10/12	27/05
2020	12/08	07/10	30/12	01/06

Notes: Year shown corresponds to the end of the ice season. Imaging started at the end of 2008 hence no imagery was available for the ice-free period of 2008.

Table 2. Number of RSAT-2 SCWA scenes available per class per year.

Year	Ice-free		Full-ice Coverage		Mixed Coverage	Total
	Train/Val	Test	Train/Val	Test
2009	–	–	3	–	53	56
2010	26	–	25	–	21	72
2011	57	–	32	–	45	134
2012	54	–	58	–	54	166
2013	52	–	58	–	76	186
2014	34	–	55	–	99	188
2015	30	–	55	–	74	159
2016	38	–	49	–	68	155
2017	60	–	45	–	77	182
2018	55	–	71	–	195	321
2019	42	–	90	–	221	353
2020	–	89	–	45	27	161
Total	448	89	541	45	1,010	2,133

Notes: Year corresponds to the same years as Table 1 where the mixed coverage corresponds to the images available after the full-ice coverage (10/10 landfast ice) period, i.e., the break-up period of the given year.

Table 3. Architecture of the convolutional neural network used.

Layer	Shape	Activation Function
Convolution 2D	128 × 5 × 5 sliding window [250 × 250m]	ReLU
Max pooling	2 × 2 sliding window [100 × 100m]
Convolution 2D	128 × 5 × 5 sliding window [500 × 500m]	ReLU
Max pooling	2 × 2 sliding window [200 × 200m]
Fully connected layer	1,024 nodes	ReLU
Fully connected layer	128 nodes	ReLU
Fully connected layer	1 node	Sigmoid

Figure 3. Backscatter intensity (σ⁰) distribution per degree of incidence angle for the HH channel. Orange line is the median, boxes correspond to the 1st and 3rd quartile and whiskers represent the 5th and 95th percentile. Red line corresponds to the ScanSAR noise floor (NESZ).

Figure 4. Backscatter intensity (σ⁰) distribution per degree of incidence angle for the HV channel. Orange line is the median, boxes correspond to the 1st and 3rd quartile and whiskers represent the 5th and 95th percentile. Red line corresponds to the ScanSAR noise floor (NESZ).

Figure 5. Convolutional neural network training and validation accuracy and binary cross-entropy loss function for each training epoch for the 4-bands input model (Table 4).

Table 4. Training and validation accuracies based on SAR input parameterization and different input layers.

Calibration	Input Layers	Training		Validation
		Accuracy	Loss	Accuracy	Loss
σ^0	HH + HV+θ+NESZ	0.911	0.194	0.901	0.217
γ^0	HH + HV+θ+NESZ	0.909	0.197	0.899	0.220
β^0	HH + HV+θ+NESZ	0.909	0.198	0.899	0.220
σ^0	HH + HV+θ	0.903	0.208	0.897	0.222
σ^0	HH + HV + NESZ	0.907	0.201	0.891	0.239
σ^0	HH + HV	0.869	0.272	0.853	0.303
σ^0	HH +θ+NESZ	0.825	0.357	0.815	0.376
σ^0	HH+θ	0.822	0.362	0.811	0.382
σ^0	HH + NESZ	0.818	0.370	0.805	0.393
σ^0	HH	0.811	0.382	0.801	0.402
σ^0	HV+θ+NESZ	0.785	0.429	0.776	0.445
σ^0	HV+θ	0.768	0.456	0.763	0.464
σ^0	HV + NESZ	0.771	0.453	0.757	0.477
σ^0	HV	0.706	0.534	0.693	0.555

Figure 6. 4-band input model (Table 4) predictions distribution per incidence angle. Orange line is the median, boxes correspond to the 1st and 3rd quartile and whiskers represent the 5th and 95th percentile.

Table 5. CNN training, validation and overall accuracies for ice and water classes independently and combined for the 4-input layer model (Table 4).

Dataset	Class
	Ice	Water	Overall
Training	0.938	0.864	0.911
Validation	0.927	0.878	0.901
Test	0.924	0.818	0.863
All	0.935	0.869	0.906

Figure 7. (a) CIS regional chart valid July 13, 2020; (b) ASI SIC product of July 13, 2020; (c) MODIS Terra image of July 14; (d) RSAT-2 RGB: HH-HV-HV composite image of July 13,2020; and (e) CNN model output for July 13, 2020.

Figure 8. Timeseries of weekly SIC for all of Coronation Gulf from the CIS regional charts, ASI passive microwave product and output of the RSAT-2 images using the 4-band input CNN model. The values are valid on the Monday for each week. The ASI and RSAT-2 products consist of weekly pixel average.

Figure 9. Box plot of the weekly SIC biases from Figure 8 for each month for the entire time series. The CIS charts were taken as reference for the bias calculation.