Variational Fuzzy Superpixel Segmentation

Ng, Tsz Ching; Choy, Siu Kai

doi:10.1109/tfuzz.2020.3029939

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…BR measures the fraction of ground truth boundaries correctly recovered by the superpixel boundaries 30 . A high BR value indicates that very few true edges are missed, which have a better preservation effect of the generated superpixels on the image boundary.…”

Section: Experimental and Resultsmentioning

confidence: 99%

“…BR measures the fraction of ground truth boundaries correctly recovered by the superpixel boundaries. 30 A high BR value indicates that very few true edges are missed, which have a better preservation effect of the generated superpixels on the image boundary. The BR is defined as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 9 ; 1 1 4 ; 6 6 4 BRðG; SÞ ¼ TPðG; SÞ TPðG; SÞ þ FNðG; SÞ ;…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Superpixel segmentation of high-resolution remote sensing image based on feature reconstruction method by salient edges

Wang

et al. 2023

J. Appl. Rem. Sens.

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Superpixel segmentation is an important image preprocessing technology that can aggregate adjacent pixels with similar characteristics to reduce the complexity of subsequent tasks. However, with the increasing spatial resolution of remote sensing images, maintaining accurate boundary information of ground objects during superpixel segmentation has become a challenge. We propose a feature reconstruction method based on edge detection and adaptive morphological reconstruction to enhance feature representation for superpixel segmentation. Specifically, we employ the structured forest model to extract robust edges from the image, and an adaptive morphological reconstruction method is proposed to eliminate noise from the edge information. Furthermore, a feature fusion method based on wavelet transform is constructed to integrate the extracted edge information with the original image, which further enhances the segmentation accuracy of image edges in superpixel segmentation. Experimental results on three datasets of natural and remote sensing images demonstrate that the proposed method can effectively construct the feature representation of images and achieve better superpixel segmentation results than other methods.

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Section: Experimental and Resultsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Superpixel segmentation of high-resolution remote sensing image based on feature reconstruction method by salient edges

Wang

et al. 2023

J. Appl. Rem. Sens.

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show abstract

“…In order to verify the effectiveness of the proposed image segmentation method, the boundary constraints and texture features on the segmentation algorithm are tested by ablation experiments. A subset of the region from dataset-1 was chosen as the experimental data, and the boundary recall rate [72] was employed as an evaluation metric for assessing the segmentation effect. The boundary recall rate serves as a metric to quantify the alignment between the segmentation boundary and the actual boundary.…”

Section: A Comparison Of Image Segmentation Methodsmentioning

confidence: 99%

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Zhang,

Liu,

Niu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Vector Polygons are valuable survey data, serving as crucial outputs of national geographical censuses and a fundamental data source for detecting changes in geographical conditions. Current remote-sensing image change detection methods rely on comparing images but overlook abundant historical vector results, struggle with model generalization, and lack adequate samples. Consequently, change detection remains a manual process primarily, unable to meet the requirements for automated and efficient monitoring of standardized geographical conditions. Hence, this paper proposes a change detection method for land cover vector polygons based on high-resolution remote sensing images and deep learning. Initially, the enhanced simple linear iterative clustering (SLIC) algorithm is applied to segment dual-temporal images from identical regions. Subsequently, an annotated dataset is generated using a multi-scale extraction, cropping-with-inpainting approach. Next, datasets derived from pre-and post-temporal images are used for training and testing, respectively, and the training set is purified by using twoclassifier cross-validation. Finally, an improved object-oriented convolutional neural network (CNN) model performs finegrained scene classification. The change rules and postprocessing method are then integrated to identify changed vector polygons. To validate the effectiveness and superiority of the proposed method, we conducted experiments on land cover change detection using datasets from two study areas. The results indicate that the proposed method achieves precision and recall rates of 91.89% and 94.44% on dataset-1, respectively. Similarly, in dataset-2, the precision and recall rates reach 87.59% and 91.41%, respectively. These findings demonstrate the method's efficacy in detecting changed vector polygons, reducing manual intervention, and enhancing detection efficiency.

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Robust superpixel-based fuzzy possibilistic clustering method incorporating local information for image segmentation

Wu,

Zhao

2023

Vis Comput

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Variational Fuzzy Superpixel Segmentation

Cited by 8 publications

References 38 publications

Superpixel segmentation of high-resolution remote sensing image based on feature reconstruction method by salient edges

Superpixel segmentation of high-resolution remote sensing image based on feature reconstruction method by salient edges

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Robust superpixel-based fuzzy possibilistic clustering method incorporating local information for image segmentation

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