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
Online-Zugang:	Volltext
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