Color image segmentation using mean shift filtering and wavelet frames

Özden, Mustafa; Polat, Ediz; Tuna, Eyup

doi:10.1109/siu.2005.1567677

Cited by 1 publication

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early semantic segmentation research focused on non-DL models, such as the threshold method (Davis et al, 1975), the clustering-based method (Özden and Polat, 2005), the edge detection method (Senthilkumaran and Rajesh, 2009), and conditional random fields (CRFs) (Nowozin and Lampert, 2011). These traditional methods have low efficiency and accuracy.…”

Section: Methods Overviewmentioning

confidence: 99%

“…DL is gradually being applied to RS semantic segmentation (Piramanayagam et al, 2018;Sun et al, 2019). Early methods based on sliding windows and candidate regions are time-consuming and have a lot of redundant calculations (Davis et al, 1975;Özden and Polat, 2005;Senthilkumaran and Rajesh, 2009;Nowozin and Lampert, 2011;Ciresan et al, 2012). In recent years, there are more and more DL-based methods in RS, including U-Net methods and its variants (Yue et al, 2019;Foivos et al, 2020), multi-scale context aggregation networks (Liu et al, 2018a;Chen et al, 2020;Xu H. et al, 2022), and multi-level feature fusion networks (Dong and Chen, 2021;Li et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning-based semantic segmentation of remote sensing images: a review

Shen

et al. 2023

Front. Ecol. Evol.

View full text Add to dashboard Cite

Semantic segmentation is a fundamental but challenging problem of pixel-level remote sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite images play an important role in a wide range of applications. Recently, with the successful applications of deep learning (DL) in the computer vision (CV) field, more and more researchers have introduced and improved DL methods to the task of RS data semantic segmentation and achieved excellent results. Although there are a large number of DL methods, there remains a deficiency in the evaluation and advancement of semantic segmentation techniques for RS data. To solve the problem, this paper surveys more than 100 papers in this field in the past 5 years and elaborates in detail on the aspects of technical framework classification discussion, datasets, experimental evaluation, research challenges, and future research directions. Different from several previously published surveys, this paper first focuses on comprehensively summarizing the advantages and disadvantages of techniques and models based on the important and difficult points. This research will help beginners quickly establish research ideas and processes in this field, allowing them to focus on algorithm innovation without paying too much attention to datasets, evaluation indicators, and research frameworks.

show abstract