Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery

Andrea Federici; Jakob Grahn; Giacomo Boracchi; Filippo Maria Bianchi

cs.CVAug 14, 2026

Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery

Authors: Andrea Federici, Jakob Grahn, Giacomo Boracchi, Filippo Maria Bianchi

Organizations: Dept. of Mathematics and Statistics, UiT the Arctic University of Norway · NORCE, The Norwegian Research Centre AS · DEIB, Politecnico di Milano

Abstract

Polar lows are intense maritime cyclones that form rapidly at high latitudes. Deep learning can detect them in Synthetic Aperture Radar (SAR) imagery, but pixel-level segmentation remains an open challenge. No pixel-level masks are available for training, and a polar low's extent is inherently subjective, with diffuse boundaries that even experts delineate inconsistently. We propose Constrained Region Erasing with Soft Targets (CREST), a Weakly Supervised Semantic Segmentation (WSSS) framework that generates masks solely from image-level labels. Our approach builds on Adversarial Erasing (AER), which iteratively mines discriminative regions, erases them, and retrains a classifier to reveal complementary cues that become pseudo-labels for segmentation. However, standard AER also collects irrelevant background features, degrading pseudo-label quality. CREST addresses this with (i) a Constrained Ordinal Region Expansion (CORE) module that encodes the spatial-connectedness prior of polar lows, constraining region expansion from a high-confidence seed, and (ii) a Dynamic Bootstrapping (DB) loss that treats the mining order as a proxy for label reliability, attenuating supervision from noisier, later-mined regions. On Sentinel-1 SAR data, CREST follows the cyclone structure more closely than standard AER, and returns a multi-class rather than binary mask whose classes indicate the reliability assigned to each region. We further evaluate on BUS-UCLM breast ultrasound and PASCAL VOC person data, whose targets satisfy the same connectedness prior but come with the dense masks the SAR data lacks. On both datasets, CREST performs better than the equivalent AER pipeline under identical settings.

Figures & tables

Panel of Figure 1 — Fig. 1 : Examples of Sentinel-1 SAR imagery from our dataset. (a) A positive sample showing a polar low. (b) A negative sample showing a typical maritime background without cyclonic activity. The black regions are “no-data” areas in the SAR images.

Panel of Figure 2 — Fig. 2 : Spurious background activations during late AER iterations. Each row shows a different positive polar low SAR image. Left: original input. Right: Grad-CAM on the image after erasing mined regions. With most of the target regions removed, the classifier starts producing activations on the background, injecting noise into the aggregated pseudo-label.

Panel of Figure 3 — Fig. 3 : Impact of CORE on region expansion. The panels display the Grad-CAM saliency maps at each step of the mining process. Once activated, CORE introduces the yellow local envelope that limits subsequent mask growth. In Iteration 3, the classifier produces a strong activation on land outside this envelope; the activation is therefore excluded from the next mask update, as shown in Iteration 4.

Fig. 4 : AER+CORE pipeline and pseudo-label construction. Top: the proposed iterative train $\rightarrow$ extract $\rightarrow$ erase loop. We retrain the classifier after each erasing step and recompute Grad-CAM on the updated image. Once activated, CORE restricts the next erasure to a neighborhood (yellow envelope) around the previously mined region. Bottom: Grad-CAM heatmaps from successive iterations and the resulting multi-class pseudo-label, where the class records when each region was mined.

Panel of Figure 5 — Fig. 5 : Grad-CAM and Integrated Gradients on a Sentinel-1 SAR image. In this example, both methods highlight the most discriminative cues but not the full spatial extent of the cyclone.

Panel of Figure 6 — Fig. 6 : Zero-shot SAM outputs for one Sentinel-1 polar low SAR image. With grid- and box-based prompts, the model highlights imaging artifacts or misses the diffuse cyclone structure.

Fig. 7 : Overview of the proposed two-stage pipeline. Stage 1 generates mining-order pseudo-labels from image-level labels via AER with the CORE module. Stage 2 trains a segmentation network using these pseudo-labels and a Dynamic Bootstrapping strategy to handle label uncertainty.

Fig. 8 : Effect of dynamic bootstrapping under the same background-biased model prediction

\mathbf{p}=[0.9,0.08,0.02]

\gamma(e)=0.5

. The bootstrapped target mixes the hard pseudo-label

\mathbf{y}

with the detached prediction

\operatorname{sg}(\mathbf{p})

. For the high-reliability case,

\rho_{s}=0.9

and

\lambda_{s}(e)=0.05

, corresponding to CE gradient

\times\,0.95

(

5\%

attenuation); for the low-reliability case,

\rho_{s}=0.2

and

\lambda_{s}(e)=0.40

, corresponding to CE gradient

\times\,0.60

(

40\%

attenuation).

Panel of Figure 9 — Fig. 9 : Visual comparison of segmentation masks for maritime mesocyclones in SAR images. First column: input images. Second: segmentation network trained with single-pass Grad-CAM masks, which concentrate mainly around the cyclone eye in these examples. Third: standard AER extends the prediction to more peripheral regions, but can also incorporate background. Fourth: CREST predictions, with colors encoding mining order.

Fig. 10 : Illustrative case: a single SAR acquisition overlaid with auxiliary meteorological fields.

Method	BUS						VOC
	Macro		Foreground				Macro		Foreground
	IoU $\uparrow$	Dice $\uparrow$	IoU $\uparrow$	Dice $\uparrow$	Precision $\uparrow$	Recall $\uparrow$	IoU $\uparrow$	Dice $\uparrow$	IoU $\uparrow$	Dice $\uparrow$	Precision $\uparrow$	Recall $\uparrow$
Grad-CAM	57.2	64.2	17.7	30.1	62.4	19.8	51.8	54.8	5.7	10.7	39.8	6.2
Standard AER	60.2	68.6	24.3	39.1	45.0	34.6	65.4	74.4	33.5	50.2	38.8	71.3
CREST	64.8	73.9	32.7	49.3	58.7	42.5	67.4	76.6	37.5	54.6	41.1	81.0

TABLE I : Best test performance on BUS and VOC . For the iterative methods, we report the mining iteration that achieved the best Macro IoU; Tables IV and V report all iterations. Best results per metric are highlighted in bold.

Panel of Figure 12 — Fig. 11 : BUS qualitative examples. The predicted multi-class masks preserve the mining order from AER : warmer colors (yellow) indicate earlier-mined, more discriminative regions, while cooler colors (blue) indicate regions discovered later. In the displayed examples, the earliest tiers fall on the lesion core and later tiers extend toward its margin, and the ground-truth masks are shown for visual comparison.

Panel of Figure 13 — Fig. 12 : VOC qualitative examples for the person class. Colors in the multi-class output indicate mining order: warmer colors are earlier discoveries and cooler colors are later discoveries. In these examples, early discoveries tend to emphasize discriminative features such as the face and upper body, while later discoveries extend to other body regions. The binarized output is used for evaluation against the ground-truth mask.

Method	BUS				VOC
	Macro IoU $\uparrow$	Macro Dice $\uparrow$	IoU c=1 $\uparrow$	Dice c=1 $\uparrow$	Macro IoU $\uparrow$	Macro Dice $\uparrow$	IoU c=1 $\uparrow$	Dice c=1 $\uparrow$
Standard AER	60.2	68.6	24.3	39.1	65.4	74.4	33.5	50.2
AER+CORE	61.4	70.0	26.4	41.8	66.5	75.6	35.6	52.5
AER+CORE+DB	64.8	73.9	32.7	49.3	67.4	76.6	37.5	54.6

TABLE II : Progressive ablation results on the BUS and VOC test sets, where each row shows the effect of adding components to the baseline AER pipeline. For each method, we report the mining iteration that achieved the best Macro IoU.

Fig. 13 : Macro IoU over retained adversarial-erasing iterations on the BUS test set. Standard AER degrades after iteration 2, whereas the AER+CORE and full CREST (AER+CORE+DB) trajectories vary less over the later iterations.

Fig. 14 : Discrete precision–recall trajectories on the VOC test set as additional mining iterations are retained. Numbers indicate the iteration index. In the legend, AER+CORE+DB is the full CREST configuration.

Method	BUS		VOC
	Macro IoU $\uparrow$	IoU c=1 $\uparrow$	Macro IoU $\uparrow$	IoU c=1 $\uparrow$
Standard AER	57.5	18.6	65.7	34.4
CREST	59.6	22.7	68.4	38.8

TABLE III : Ablation results using Grad-CAM++ attribution on the BUS and VOC test sets. For each method, we report the mining iteration that achieved the best Macro IoU (standard AER at

t=2

and CREST at

t=3

on BUS ; both at

t=6

on VOC ).

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 18 — Fig. 15 : Mosaic of inputs and predicted masks for test images. Warmer colors (red/yellow) denote earlier-mined regions; cooler colors (blue/cyan) denote later-mined regions.

Panel of Figure 19 — Fig. 16 : Qualitative consistency under geometric transformations. Left: original image. Right: predictions for the original and three transformed versions.

Method	Macro IoU $\uparrow$	Macro Dice $\uparrow$	IoU c=1 $\uparrow$	Dice c=1 $\uparrow$	Precision c=1 $\uparrow$	Recall c=1 $\uparrow$
Grad-CAM	57.2	64.2	17.7	30.1	62.4	19.8
Standard AER
Iteration 0	49.2	51.0	2.0	3.9	70.6	2.0
Iteration 1	57.6	64.7	18.3	31.0	67.0	20.1
Iteration 2	60.2	68.6	24.3	39.1	45.0	34.6
Iteration 3	58.0	66.5	22.0	36.0	29.6	46.1

Appendix

TABLE IV : Per-iteration results on the BUS test set. For the iterative methods, bold marks the best value per column across mining iterations.

Method	Macro IoU $\uparrow$	Macro Dice $\uparrow$	IoU c=1 $\uparrow$	Dice c=1 $\uparrow$	Precision c=1 $\uparrow$	Recall c=1 $\uparrow$
Grad-CAM	51.8	54.8	5.7	10.7	39.8	6.2
Standard AER
Iteration 0	52.2	55.5	6.3	11.9	60.8	6.6
Iteration 1	60.3	68.0	22.8	37.1	44.3	31.9
Iteration 2	63.7	72.4	29.8	45.9	42.8	49.5
Iteration 3	65.1	74.0	32.8	49.4	40.5	63.3

Appendix

TABLE V : Per-iteration results on the VOC test set (person class). For the iterative methods, bold marks the best value per column across mining iterations.

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Abstract

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