Skip to Main content Skip to Navigation
New interface
Conference papers

LAKE DETECTION WITH SENTINEL-1 DATA USING A GRAB-CUT METHOD AND ITS MULTI-TEMPORAL EXTENSION

Abstract : This paper presents a semi-guided method to detect lakes in Sentinel-1 SAR data. The proposed approach is an adaptation of the grab-cut framework developed in [1]. Starting from a coarse bounding box around the lake, an accurate segmentation is extracted using a Conditional Random Field formalism and a graph-cut based optimization. Then an extension of this approach to process jointly a stack of multi-temporal data is presented. A temporal regularization term is introduced to control the joint segmentation. The proposed approach is evaluated on Sentinel-1 datasets. Qualitative and quantitative results demonstrate the interest of the proposed framework and its robustness to the initialization polygon of the lake.
Complete list of metadata

https://hal.telecom-paris.fr/hal-03756052
Contributor : Florence Tupin Connect in order to contact the contributor
Submitted on : Monday, August 22, 2022 - 11:15:32 AM
Last modification on : Tuesday, August 30, 2022 - 3:41:07 AM
Long-term archiving on: : Wednesday, November 23, 2022 - 8:14:28 PM

File

IGARSS2022_GRABCUT_SAR_2DplusT...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03756052, version 1

Collections

Citation

Nicolas Gasnier, Loïc Denis, Roger Fjørtoft, Frédéric Liege, Florence Tupin. LAKE DETECTION WITH SENTINEL-1 DATA USING A GRAB-CUT METHOD AND ITS MULTI-TEMPORAL EXTENSION. IGARSS, 2022, Kuala Lumpur, Malaysia. ⟨hal-03756052⟩

Share

Metrics

Record views

15

Files downloads

5