Search Results - ((canopy algorithm) OR (means algorithm))

Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

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Individual-tree segmentation and extraction based on LiDAR point cloud data by Liu, Xiaofeng, Abdullah, Muhamad Taufik, Mustaffa, Mas Rina, Nasharuddin, Nurul Amelina

“…However, in terms of tree height precision, as measured by the coefficient of determination and root mean square error (RMSE), the watershed algorithm proved superior. …”
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Performance of a photogrammetric digital elevation model in a tropical montane forest environment by Wong, Wilson, Vun Chiong, Satoshi Tsuyuki, Phua, Mui How, Keiko Ioki, Gen Takao

“…More detailed local accuracy evaluation was conducted at block level: 31 1-ha blocks and one 0.25-ha block located over different forest types and characterized by the mean canopy height (range=8.4-41.1 m) and standard deviation (range=2.0-9.8 m) of the ALS-canopy height model (ALS-CHM). …”
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Detecting canopy openings in logged-over forests: a multi-classifier analysis of PlanetScope imagery by Mawlidan, Nurmala, Ismail, Mohd Hasmadi, Gandaseca, Seca, ., Rahmawaty, Yaakub, Nur Faziera

“…Both SVM and OBIA surpassed the accuracy threshold, with SVM being the most effective in detecting and extracting canopy openings in dense forests. Results demonstrated the potential of PlanetSope imagery and advanced classification algorithms to detect canopy openings in logged-over forests. …”
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Photogrammetric unmanned aerial vehicle for digital terrain model estimation under oil palm tree canopy area / Suzanah Abdullah by Abdullah, Suzanah

“…It was found that WS algorithm recorded the lower RMSE value of 2.824m at 40m flying height, IWS algorithm obtained a lower RMSE value of 2.879m at 80m flying height, OBIA and SG algorithms obtained a lower RMSE value of 2.246m and 2.182m respectively at 100m flying height. …”
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification by Myaser, Jwan

“…The last phase involves developing a new fusion algorithm using SVM and Fuzzy K-Means Clustering (FKM) algorithms for Sentinel 2 data to enhance LCM accuracy. …”
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Extracting crown morphology with a low-cost mobile LiDAR scanning system in the natural environment by Wang, Kai, Zhou, Jun, Zhang, Wenhai, Zhang, Baohua

“…The algorithm defined in this study was evaluated with manual measurements as reference, and the morphological parameters of the canopy obtained using the LOAM and LeGO-LOAM algorithms as the basic framework were compared. …”
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Evaluation of tree growth characteristics of harum manis mango from multitemporal UAV image in plantation farm by Muhammad Yusuf, Akmal Alif

“…The contribution of this study is to produce Canopy Height Mapping (CHM) from difference multitemporal UAV data and characterization of mango tree develop using OBIA algorithm to monitoring development Harum Manis Tree. …”
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A Hydrologic Model for Studying the Climate Change Impact on Evapotranspiration and Water Yield in a Humid Tropical Watershed by Nabi, Amjad

“…was used in the model as a measure of vegetation structure and to quantify energy and mass exchange of canopies. The potentials of Landsat TM image was studied for LA! …”
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Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll suffic... by Amirruddin, Amiratul Diyana, Muharam, Farrah Melissa, Ismail, Mohd Hasmadi, Tan, Ngai Paing, Ismail, Mohd Firdaus

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Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid by Khalid, Nurul Syafiqah

“…This study aimed to develop the automatic oil palm tree counting using remote sensed data and two different algorithms at Felda Pasoh. There are three objectives, firstly, to produce tree height estimation by using the Canopy Height Model (CHM). …”
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Improved clustering using robust and classical principal component by Hassn, Ahmed Kadom

“…The classical k-means algorithm and the k-means by PCA algorithm are very sensitive to the presence of outlier. …”
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A novel quantum calculus-based complex least mean square algorithm (q-CLMS) by Sadiq, A., Naseem, I., Khan, S., Moinuddin, M., Togneri, R., Bennamoun, M.

“…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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A novel quantum calculus-based complex least mean square algorithm (q-CLMS) by Sadiq, A., Naseem, I., Khan, S., Moinuddin, M., Togneri, R., Bennamoun, M.

“…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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A novel quantum calculus-based complex least mean square algorithm (q-CLMS) by Sadiq, A., Naseem, I., Khan, S., Moinuddin, M., Togneri, R., Bennamoun, M.

“…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

“…Among the techniques, the k-means algorithm is the most commonly used technique for determining optimal number of clusters (k). …”
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Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Tutut, Herawan, K., F.Rabbi

“…K-Means is one of the unsupervised learning and partitioning clustering algorithms. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Fazley Rabbi, Khandakar

“…K-Means is one of the unsupervised learning and partitioning clustering algorithms. …”
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Autonomous and deterministic supervised fuzzy clustering by Lim, K.M., Loo, C.K., Lim, W.S.

“…This algorithm implements k-means to initialize the fuzzy model. …”
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