Investigation of the random forest framework for classification of hyperspectral data

By Jisoo Ham, Yangchi Chen, Melba M. Crawford, Joydeep Ghosh. IEEE Transactions on Geoscience and Remote Sensing, 2005

1,210 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/3b3d39bfcdd976edc63dbaa4c65c47f314cc5d6c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Artificial Intelligence

In Media Technology

Citations in the corpus

Domain Adaptation under Target and Conditional Shift (2013, methodology): “, 2008; Huang et al., 2007) or sample selection bias (Zadrozny, 2004), and has been well studied. For surveys on domain adaptation for classification, see, e.g., Jiang (2008); Pan & Yang (2010); Candela et al.”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, background): “As such, many research works have illustrated the great capability of the RS classiﬁer for classiﬁcation of Landsat archive, hyper and multispectral image classiﬁcation [135], and digital elevation model data [136].”
Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis (2009, background): “For more information, see [35] and visit http://www.”
The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees (2010, methodology): “RF trees were considered a valid comparison to SVM as they are not subject to overfitting (Breiman 2001) and often out-perform other tree based classifiers (Ham et al. 2005, Gislason et al. 2006).”
Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification (2019, background): “Bayesian models [16], random forest (RF) [17], and neural networks [18] are amongst the most popular approaches.”
Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images (2011, background): “The reader is referred to [40] for greater details on this dataset.”
MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification (2025, methodology): “A. HSI Classification HSI classification methods typically fall into ML-based [10], [30] and DL-based [13], [15].”
Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification (2017, methodology): “Research in the last decades has resulted in many popular models, such as neural networks [7], [8], the Gaussian process approach [9], conditional random fields [10], [11], the graph method [12], [13], multinomial logistic regression [14], [15], support vector machines (SVMs) [16], AdaBoost [17], an”
Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification (2020, methodology): “We compare classical machine learning and representative deep learning methods available in [63]2: random forest (RF) [64], multinomial logistic regression (MLR) [65], support vector machine (SVM) with radial basis function [1], gated recurrent unit (GRU) [66], long short term memory (LSTM) [67], Re”
Multisource Remote Sensing Data Classification Based on Convolutional Neural Network (2018, background): “Random forest, with the strategy of boot-bagging [13], [28], provides satisﬁed performance in remote sensing data classiﬁcation owing to its tolerance on big data.”
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019, methodology): “This is the case, for instance, of Scale-Invariant Feature Transform (SIFT) [12], Histogram of Oriented Gradients (HOG) [13] or Local Binary Patterns [14], in conjunction with kernel-based Support Vector Machines (SVM) [15], Random Forests [16] or statistical learning methods [17].”
Mining data with random forests: A survey and results of new tests (2011, background): “RF have been used for large variety of tasks, including: identification of DNA-binding proteins [13], segmentation of video objects [14], classification of hyper-spectral data [15,16], prediction of the vegetation type occurrence in Belgian lowland valley based on spatially distributed measurements ”
Capsule Networks for Hyperspectral Image Classification (2019, background): “In particular, some of the most noteworthy approaches rely on support vector machines (SVMs) [11], k-means clustering [12], Gaussian process [13], random forest (RF) [14], extreme learning machines [15], and deep neural network classifiers [16].”