Gamma Correction-Based Automatic Unsupervised Change Detection in SAR Images Via FLICM Model

Li, Liangliang; Ma, Hongbing; Jia, Zhenhong

doi:10.1007/s12524-023-01674-4

Cited by 4 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This formula indicates that F1 will be high only when both Prec and Rec are high. Thus, the higher the F1, the more effective the model's performance [50,51]. mIoU, also known as the Jaccard similarity coefficient (JSC), refers to the mean intersection over union, which is the most commonly used semantic segmentation metric.…”

Section: Quantitative Resultsmentioning

confidence: 99%

FCNet: Flexible Convolution Network for Infrared Small Ship Detection

Guo,

Ma,

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

The automatic monitoring and detection of maritime targets hold paramount significance in safeguarding national sovereignty, ensuring maritime rights, and advancing national development. Among the principal means of maritime surveillance, infrared (IR) small ship detection technology stands out. However, due to their minimal pixel occupancy and lack of discernible color and texture information, IR small ships have persistently posed a formidable challenge in the realm of target detection. Additionally, the intricate maritime backgrounds often exacerbate the issue by inducing high false alarm rates. In an effort to surmount these challenges, this paper proposes a flexible convolutional network (FCNet), integrating dilated convolutions and deformable convolutions to achieve flexible variations in convolutional receptive fields. Firstly, a feature enhancement module (FEM) is devised to enhance input features by fusing standard convolutions with dilated convolutions, thereby obtaining precise feature representations. Subsequently, a context fusion module (CFM) is designed to integrate contextual information during the downsampling process, mitigating information loss. Furthermore, a semantic fusion module (SFM) is crafted to fuse shallow features with deep semantic information during the upsampling process. Additionally, squeeze-and-excitation (SE) blocks are incorporated during upsampling to bolster channel information. Experimental evaluations conducted on two datasets demonstrate that FCNet outperforms other algorithms in the detection of IR small ships on maritime surfaces. Moreover, to propel research in deep learning-based IR small ship detection on maritime surfaces, we introduce the IR small ship dataset (Maritime-SIRST).

show abstract

Section: Quantitative Resultsmentioning

confidence: 99%

FCNet: Flexible Convolution Network for Infrared Small Ship Detection

Guo,

Ma,

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Li et al introduced Gamma correction into fuzzy local information C-means clustering (FLICM) algorithm with better robustness. 14 Through the combination of bilateral filtering and FLICM algorithm, Shang et al 15 have successfully accomplished precise detection and identification of potential change regions in remote sensing imagery. This study offers a novel perspective and methodology for analyzing remote sensing images, thereby making important contributions to the advancement of relevant domains.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al. introduced Gamma correction into fuzzy local information C-means clustering (FLICM) algorithm with better robustness 14 . Through the combination of bilateral filtering and FLICM algorithm, Shang et al 15 .…”

Section: Introductionmentioning

confidence: 99%

Synthetic aperture radar image change detection based on image difference denoising and fuzzy local information C-means clustering

Wu,

Xu,

Zhu

et al. 2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Deep neural network-based synthetic aperture radar (SAR) image change detection algorithms are affected by coherent speckle noise in the original image. Existing denoising methods have predominantly focused on generating binary images based on the pre-classification of original pixels, which is insufficient in removing interfering noise. Herein, to further reduce the noise points generated in the clustering algorithm, we combined the characteristics of the fuzzy clustering algorithm, demonstrating the obvious advantages of the proposed fast and flexible denoising convolutional neural network (FFDNet-F) method. An FFDNet was used to reduce noise interference in real SAR images and improve the detection accuracy and robustness of the method. Difference operators were then drawn from the weak noise images, and fuzzy local information C-means clustering was applied for analysis to generate the change detection results. The experimental results from two real datasets and the comparative analysis with other network models demonstrated the effectiveness of this method. Simultaneously, Gaofen-3 satellite images were used to verify and analyze surface flood disasters in Zhengzhou, China. The findings of this study demonstrate a significant improvement in detection accuracy using the proposed method compared with that of other algorithms.

show abstract