Semantic change detection (SCD) extends the multiclass change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use (LCLU) categories before and after the observation intervals. This fine-grained semantic change information is very useful in many applications. Recent studies indicate that the SCD can be modeled through a triple-branch convolutional neural network (CNN), which contains two temporal branches and a change branch. However, in this architecture, the communications between the temporal branches and the change branch are insufficient. To overcome the limitations in existing methods, we propose a novel CNN architecture for the SCD, where the semantic temporal features are merged in a deep CD unit. Furthermore, we elaborate on this architecture to reason the bi-temporal semantic correlations. The resulting bi-temporal semantic reasoning network (Bi-SRNet) contains two types of semantic reasoning blocks to reason both single-temporal and cross-temporal semantic correlations, as well as a novel loss function to improve the semantic consistency of change detection results. Experimental results on a benchmark dataset show that the proposed architecture obtains significant accuracy improvements over the existing approaches, while the added designs in the Bi-SRNet further improve the segmentation of both semantic categories and the changed areas. The codes in this article are accessible at https://github.com/ggsDing/Bi-SRNet.

Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images / Ding, Lei; Guo, Haitao; Liu, Sicong; Mou, Lichao; Zhang, Jing; Bruzzone, Lorenzo. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - 60:(2022), pp. 562001401-562001414. [10.1109/tgrs.2022.3154390]

Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images

Ding, Lei;Liu, Sicong;Zhang, Jing;Bruzzone, Lorenzo
2022-01-01

Abstract

Semantic change detection (SCD) extends the multiclass change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use (LCLU) categories before and after the observation intervals. This fine-grained semantic change information is very useful in many applications. Recent studies indicate that the SCD can be modeled through a triple-branch convolutional neural network (CNN), which contains two temporal branches and a change branch. However, in this architecture, the communications between the temporal branches and the change branch are insufficient. To overcome the limitations in existing methods, we propose a novel CNN architecture for the SCD, where the semantic temporal features are merged in a deep CD unit. Furthermore, we elaborate on this architecture to reason the bi-temporal semantic correlations. The resulting bi-temporal semantic reasoning network (Bi-SRNet) contains two types of semantic reasoning blocks to reason both single-temporal and cross-temporal semantic correlations, as well as a novel loss function to improve the semantic consistency of change detection results. Experimental results on a benchmark dataset show that the proposed architecture obtains significant accuracy improvements over the existing approaches, while the added designs in the Bi-SRNet further improve the segmentation of both semantic categories and the changed areas. The codes in this article are accessible at https://github.com/ggsDing/Bi-SRNet.
2022
Ding, Lei; Guo, Haitao; Liu, Sicong; Mou, Lichao; Zhang, Jing; Bruzzone, Lorenzo
Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images / Ding, Lei; Guo, Haitao; Liu, Sicong; Mou, Lichao; Zhang, Jing; Bruzzone, Lorenzo. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - 60:(2022), pp. 562001401-562001414. [10.1109/tgrs.2022.3154390]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/400193
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