Roadside vegetation segmentation with Adaptive Texton Clustering Model

Zhang, Ligang; Verma, Brijesh

doi:10.1016/j.engappai.2018.10.009

Cited by 7 publications

(2 citation statements)

References 49 publications

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“…Farooq et al [42] used a Convolutional Neural Network (CNN) to learn middle and high level spatial features for weed classification. Zhang and Verma [43] presented an adaptive texton clustering model and ANN classifiers for segmenting vegetation from real-world roadside image scenes. Chen et al [44] proposed an improved CNN to extract fine spatial distribution information.…”

Section: Introduction a Motivation And Objectivementioning

confidence: 99%

Vegetation Land Use/Land Cover Extraction From High-Resolution Satellite Images Based on Adaptive Context Inference

et al. 2020

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In this paper, automatic extraction of multi-context and multi-scale land use/land cover vegetation from high-resolution remote sensing images is tackled, aiming to solve typical challenges in classifying remote sensing images at a pixel level. To solve small inter-class differences and large intraclass differences between the vegetation and background, we introduce a vegetation-feature-sensitive focus perception (FP) module. Considering the intrinsic properties of vegetation objects, we established an adaptive context inference (ACI) model under a supervised setting that includes a context model to represent relationships between a center pixel and its neighbors under semantic constraints, as well as the spatial structures of vegetation features. Comparative experiments on the ZY-3 and Gaofen Image Dataset (GID) datasets demonstrate the effectiveness of our proposed automatic vegetation extraction model against the baseline Deeplab v3+ model. Taking precision, kappa coefficient, mean intersection over union (miou), precision rate, and F1-score as the evaluation indexes, the results showed an improvement in the precision by at least 1.44% and miou by 2.47%, over the baseline Deeplab v3+ model. In addition, the ACI module improved the precision and miou by 2% and 3.88%, and the FP module improved the precision and miou by 1.13% and 1.65%. These results and statistics of these comprehensive experiments illustrated that our adaptive and effective vegetation extraction model could satisfy different requirements of land use/land cover mapping applications.

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Section: Introduction a Motivation And Objectivementioning

confidence: 99%

Vegetation Land Use/Land Cover Extraction From High-Resolution Satellite Images Based on Adaptive Context Inference

et al. 2020

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“…As unsupervised learning, clustering is greatly influenced by similarity measurement and is closely related with priors in application fields. Clustering is extensively applied in fields such as biology [1,2], computer vision [3,4], geological exploration [5][6][7], and information retrieval [8], because it shows excellent advantages in automatic grouping.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Clustering Based on Centroid Learning and Cognitive Feature Capturing

Zhang

2019

Mathematical Problems in Engineering

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As one of the typical clustering algorithms, heuristic clustering is characterized by its flexibility in feature integration. This paper proposes a type of heuristic algorithm based on cognitive feature integration. The proposed algorithm employs nonparameter density estimation and maximum likelihood estimation to integrate whole and local cognitive features and finally outputs satisfying clustering results. The new approach possesses great expansibility, which enables priors supplement and misclassification adjusting during clustering process. The advantages of the new approach are as follows: (1) it is effective in recognizing stable clustering results without priors given in advance; (2) it can be applied in complex data sets and is not restricted by density and shape of the clusters; and (3) it is effective in noise and outlier recognition, which does not need elimination of noises and outliers in advance. The experiments on synthetic and real data sets exhibit better performance of the new algorithm.

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