Filtering of remote sensing point clouds using fuzzy C-means clustering

Salah, Mahmoud

doi:10.1007/s12518-020-00299-3

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…Some studies have utilized point cloud data in land cover and land use classification. Salah proposed a point-clouds-based approach to detect LULC changes from light detection and ranging (LiDAR) images, where point-cloud data was extracted from stereo satellite imagery [142]. Tseng introduced a Waveform-based classifier for clarifying and classifying point clouds of LULCs [143].…”

Section: Point Clouds For Lccdmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain.

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Section: Point Clouds For Lccdmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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“…A photogrammetric DSM with a rasterized ground sampling distance (GSD) of 0.5 m, depicted in Figure 3, was generated using semi-global matching (SGM) Hirschmüller [42] on six pan-chromatic WorldView-1 images acquired on two different days, following the workflow of d'Angelo et al [35]. The ground-truth data for the DSM filtering task, i.e., building geometries of a virtual city model in LOD2 filled with a DEM, was obtained from a CityGML model freely available through the Berlin Open Data portal 1 . In order to convert the data to a suitable form, mainly the strategy introduced by Bittner et al [8] was applied.…”

Section: Data Processingmentioning

confidence: 99%

A generalized multi-task learning approach to stereo DSM filtering in urban areas

Lukas

Bittner

2020

ISPRS Journal of Photogrammetry and Remote Sensing

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City models and height maps of urban areas serve as a valuable data source for numerous applications, such as disaster management or city planning. While this information is not globally available, it can be substituted by digital surface models (DSMs), automatically produced from inexpensive satellite imagery. However, stereo DSMs often suffer from noise and blur. Furthermore, they are heavily distorted by vegetation, which is of lesser relevance for most applications. Such basic models can be filtered by convolutional neural networks (CNNs), trained on labels derived from digital elevation models (DEMs) and 3D city models, in order to obtain a refined DSM. We propose a modular multi-task learning concept that consolidates existing approaches into a generalized framework. Our encoder-decoder models with shared encoders and multiple task-specific decoders leverage roof type classification as a secondary task and multiple objectives including a conditional adversarial term. The contributing single-objective losses are automatically weighted in the final multi-task loss function based on learned uncertainty estimates. We evaluated the performance of specific instances of this family of network architectures. Our method consistently outperforms the state of the art on common data, both quantitatively and qualitatively, and generalizes well to a new dataset of an independent study area.

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“…K means is found to be a better option for exclusive clustering but does not use local spatial statistics of the pixels. Fuzzy c means [22][23][24][25][26][27][28] is a soft clustering method where the division of image into clusters is based on membership function. But FCM method is found to be sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

Srivastava¹,

Kumar²,

Mishra³

et al. 2020

RIA

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This paper proposes a single framework for segmentation of abnormalities for breast cancer detection from Ultrasound images in presence of Rayleigh noise i.e. noise removal and segmentation are embedded in single step. It accomplishes dual purpose in a single framework simultaneously for the preprocessing and segmentation. The proposed framework comprises of two terms, first term, is used for segmentation which is a modified fuzzy c-means segmentation (MFCM) approach while second term is an adaptive complex diffusion based non linear filter (ACDPDE) that performs as regularization function for removal of Rayleigh noise, enhancement, and edge preservation of ultrasound Image. The various existing segmentation methods viz. K-Means, Texture based, Fuzzy C-Means (FCM), total variation based FCM (TVFCM), Adaptive fourth order PDE based FCM (AFPDEFCM), and the proposed method are evaluated for 50 sample ultrasound images of breast cancer. The region of interest (ROI) segmented image of ultrasound breast tissue is compared with ground truth images. From the acquired results and its analysis, it is observed that the proposed method is more robust and provides better segmentation result for ultrasound images in terms of various performance measures such as Global Constancy error (GCE), Tanimoto coefficient, Variation of Information (VOI), Probability Random Index (PRI), Jaccard coefficient, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR), dice index, False Negative Rate (FNR), and Area under curve (AUC). The proposed approach is capable of handling segmentation problem of blocky artifacts while achieving good tradeoff between Rayleigh noise removal and edge preservation. The proposed method may be useful for finding additional 33% cases of breast cancer which is missed or not detected by mammography.

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Filtering of remote sensing point clouds using fuzzy C-means clustering

Cited by 9 publications

References 33 publications

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

A generalized multi-task learning approach to stereo DSM filtering in urban areas

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

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