FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3207045857> ?p ?o ?g. }

• W3207045857 endingPage "220" @default.
• W3207045857 startingPage "195" @default.
• W3207045857 abstract "In recent years, Machine Learning (ML) algorithms have gained much attention and found a profound importance in processing, classification as well as analysis of multispectral, and hyperspectral remotely sensed data. The core objectives of this chapter are firstly to provide a critical review on important advanced ML algorithms in remote sensing data classification, and analysis; secondly, examine the performance of widely used important supervised ML algorithms namely Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) in satellite image classification, and analysis on Google Earth Engine (GEE) platform to derive distinct Land Use/Land Cover (LULC) classes. ML algorithms are being extensively used in optical remote sensing data analysis it includes the image classification algorithms to precisely allocate objects to a distinct set of known classes, the clustering algorithms to group the objects into classes based on a given set of input variables, the regression algorithms to forecast a response variable from a given a set of covariates, and the dimensionality reduction algorithms to build a small set of new variables that includes most of the information available in the input set of numerous variables. In the study, among the three tested supervised ML algorithms in LULC classification, CART algorithm shows relatively better performance than the RF, and SVM algorithms. The study concludes that advanced ML algorithms have immense potential in optical remote sensing data classification, and analysis to attain the higher classification accuracy." @default.
• W3207045857 created "2021-10-25" @default.
• W3207045857 creator A5003647913 @default.
• W3207045857 creator A5036173883 @default.
• W3207045857 date "2021-10-12" @default.
• W3207045857 modified "2023-09-27" @default.
• W3207045857 title "Machine Learning Algorithms for Optical Remote Sensing Data Classification and Analysis" @default.
• W3207045857 cites W1471436312 @default.
• W3207045857 cites W150074832 @default.
• W3207045857 cites W1580493526 @default.
• W3207045857 cites W1663792126 @default.
• W3207045857 cites W1964425467 @default.
• W3207045857 cites W1967335776 @default.
• W3207045857 cites W1967722715 @default.
• W3207045857 cites W1973249074 @default.
• W3207045857 cites W1974981350 @default.
• W3207045857 cites W1980547211 @default.
• W3207045857 cites W1994214164 @default.
• W3207045857 cites W1995450389 @default.
• W3207045857 cites W1998979050 @default.
• W3207045857 cites W2001298023 @default.
• W3207045857 cites W2004206930 @default.
• W3207045857 cites W2010319424 @default.
• W3207045857 cites W2019305916 @default.
• W3207045857 cites W2023155255 @default.
• W3207045857 cites W2024079685 @default.
• W3207045857 cites W2029324310 @default.
• W3207045857 cites W2031392670 @default.
• W3207045857 cites W2035222601 @default.
• W3207045857 cites W2041657594 @default.
• W3207045857 cites W2049582104 @default.
• W3207045857 cites W2051165725 @default.
• W3207045857 cites W2053515681 @default.
• W3207045857 cites W2059423883 @default.
• W3207045857 cites W2061581247 @default.
• W3207045857 cites W2065040528 @default.
• W3207045857 cites W2065800647 @default.
• W3207045857 cites W2071470674 @default.
• W3207045857 cites W2080680225 @default.
• W3207045857 cites W2081346329 @default.
• W3207045857 cites W2082081125 @default.
• W3207045857 cites W2084710983 @default.
• W3207045857 cites W2084975575 @default.
• W3207045857 cites W2089806346 @default.
• W3207045857 cites W2102731100 @default.
• W3207045857 cites W2109191549 @default.
• W3207045857 cites W2110549418 @default.
• W3207045857 cites W2111256709 @default.
• W3207045857 cites W2118823101 @default.
• W3207045857 cites W2124706543 @default.
• W3207045857 cites W2130269771 @default.
• W3207045857 cites W2130774035 @default.
• W3207045857 cites W2132122502 @default.
• W3207045857 cites W2138499468 @default.
• W3207045857 cites W2138973222 @default.
• W3207045857 cites W2139212933 @default.
• W3207045857 cites W2139833307 @default.
• W3207045857 cites W2142827986 @default.
• W3207045857 cites W2143340430 @default.
• W3207045857 cites W2156909104 @default.
• W3207045857 cites W2162698522 @default.
• W3207045857 cites W2164785235 @default.
• W3207045857 cites W2167478073 @default.
• W3207045857 cites W2168809519 @default.
• W3207045857 cites W2176673053 @default.
• W3207045857 cites W2218047931 @default.
• W3207045857 cites W2224936358 @default.
• W3207045857 cites W2261059368 @default.
• W3207045857 cites W2333834816 @default.
• W3207045857 cites W2335743313 @default.
• W3207045857 cites W2525592260 @default.
• W3207045857 cites W2538244214 @default.
• W3207045857 cites W2570489808 @default.
• W3207045857 cites W2587031013 @default.
• W3207045857 cites W2592370397 @default.
• W3207045857 cites W2597644092 @default.
• W3207045857 cites W2605627592 @default.
• W3207045857 cites W2621048175 @default.
• W3207045857 cites W2646675373 @default.
• W3207045857 cites W2763734094 @default.
• W3207045857 cites W2769921143 @default.
• W3207045857 cites W2807355972 @default.
• W3207045857 cites W2887887128 @default.
• W3207045857 cites W2889804671 @default.
• W3207045857 cites W2895949749 @default.
• W3207045857 cites W2902216234 @default.
• W3207045857 cites W2954594143 @default.
• W3207045857 cites W2955914832 @default.
• W3207045857 cites W3036103835 @default.
• W3207045857 cites W3104356922 @default.
• W3207045857 cites W3215186461 @default.
• W3207045857 cites W4299689471 @default.
• W3207045857 doi "https://doi.org/10.1007/978-981-16-5847-1_10" @default.
• W3207045857 hasPublicationYear "2021" @default.
• W3207045857 type Work @default.
• W3207045857 sameAs 3207045857 @default.
• W3207045857 citedByCount "2" @default.
• W3207045857 countsByYear W32070458572023 @default.

• next