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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Vydané v:	IEEE geoscience and remote sensing letters Ročník 22; s. 1 - 5
Hlavní autori:	Fan, Linqian, Lu, Wenkai, Wang, Yonghao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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