A5057147532- Remote-Sensing Image Classification
- Remote Sensing in Agriculture
- Remote Sensing and Land Use
- Smart Agriculture and AI
- Landslides and related hazards
- Time Series Analysis and Forecasting
- Spectroscopy and Chemometric Analyses
University of Twente
2017-2020
The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known recurrently used methods for this task. In paper, we evaluate pros cons using an RF-based kernel (RFK) SVM compared to conventional Radial Basis Function (RBF) standard RF classifier. A time series seven multispectral WorldView-2 images acquired over Sukumba (Mali) single hyperspectral AVIRIS Salinas...
The classification of the ever-increasing collections remotely sensed images is a key but challenging task. In this letter, we introduce use extremely randomized trees known as Extra-Trees (ET) to create similarity kernel [ET (ETK)] that subsequently used in support vector machine (SVM) novel classifier. performance classifier benchmarked against standard ET, an SVM with both conventional radial basis function (RBF) kernel, and recently introduced random forest-based (RFK). A time series...
Random forest (RF) is a popular ensemble learning method that widely used for the analysis of remote sensing images. RF also has connections with kernel-based method. Its tree-based structure can generate an kernel (RFK) provides alternative to common kernels such as radial basis function (RBF) in methods support vector machine (SVM). Using RFK SVM been shown outperform both and SVM-RBF (i.e., using RBF SVM) classification tasks high number features. Here, we explore new designs RFKs image...
Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely costs efficient technologies to identify map crop types over large areas. Among plethora of classification methods, Support Vector Machine (SVM) Random Forest (RF) widely used because their proven performance. In this work, we study synergic use both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series...
The authors wish to make the following correction paper [...]