LSCMNet: A Lightweight Segmentation Network Based on Co-Occurring Matrix for Seismic Image

Seismic image segmentation is important in geological interpretation. In recent years, numerous studies have leveraged texture features to analyze seismic images. However, traditional texture feature extraction methods are computationally intensive and cannot be updated through back-propagation. To...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 22; S. 1 - 5
Hauptverfasser:	Fan, Linqian, Lu, Wenkai, Wang, Yonghao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Ablation Accuracy Artificial neural networks Asymmetric encoder-decoder Back propagation networks co-occurring matrix Coders Computer architecture Convolution Decoding Feature extraction Floating point arithmetic Geoscience and remote sensing Igneous rocks Image processing Image segmentation Kernel lightweight network Modules Neural networks Parameters Salt Seismic activity seismic segmentation Surface treatment Texture
ISSN:	1545-598X, 1558-0571
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Abstract	Seismic image segmentation is important in geological interpretation. In recent years, numerous studies have leveraged texture features to analyze seismic images. However, traditional texture feature extraction methods are computationally intensive and cannot be updated through back-propagation. To address these challenges, we propose a model named lightweight segmentation network based on co-occurring matrix (LSCMNet). The overall architecture of LSCMNet employs an asymmetric encoder-decoder structure. The encoder mainly consists of a lightweight bottleneck that integrates the parametric co-occurrence matrix (CM) model based on the convolutional neural network (CNN) for segmentation (S-PCMCNN) module, along with channel shuffle and split for feature fusion, enhancing the model representational capacity. The pyramid decoder encompasses a spatial attention mechanism. This design significantly reduces the parameters while maintaining accuracy in seismic image segmentation. In the application of igneous rocks, an ablation experiment was conducted to validate the effectiveness of the S-PCMCNN module. Moreover, compared with other classical segmentation models, LSCMNet demonstrates superior segmentation accuracy in few-shot scenarios while having fewer parameters and floating point operations (FLOPs).
AbstractList	Seismic image segmentation is important in geological interpretation. In recent years, numerous studies have leveraged texture features to analyze seismic images. However, traditional texture feature extraction methods are computationally intensive and cannot be updated through back-propagation. To address these challenges, we propose a model named lightweight segmentation network based on co-occurring matrix (LSCMNet). The overall architecture of LSCMNet employs an asymmetric encoder–decoder structure. The encoder mainly consists of a lightweight bottleneck that integrates the parametric co-occurrence matrix (CM) model based on the convolutional neural network (CNN) for segmentation (S-PCMCNN) module, along with channel shuffle and split for feature fusion, enhancing the model representational capacity. The pyramid decoder encompasses a spatial attention mechanism. This design significantly reduces the parameters while maintaining accuracy in seismic image segmentation. In the application of igneous rocks, an ablation experiment was conducted to validate the effectiveness of the S-PCMCNN module. Moreover, compared with other classical segmentation models, LSCMNet demonstrates superior segmentation accuracy in few-shot scenarios while having fewer parameters and floating point operations (FLOPs).
Author	Fan, Linqian Wang, Yonghao Lu, Wenkai
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SubjectTerms	Ablation Accuracy Artificial neural networks Asymmetric encoder-decoder Back propagation networks co-occurring matrix Coders Computer architecture Convolution Decoding Feature extraction Floating point arithmetic Geoscience and remote sensing Igneous rocks Image processing Image segmentation Kernel lightweight network Modules Neural networks Parameters Salt Seismic activity seismic segmentation Surface treatment Texture
Title	LSCMNet: A Lightweight Segmentation Network Based on Co-Occurring Matrix for Seismic Image
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