Random Forest Land Use Mapping from Textural and Spectral Features

Project info

• Field of studyGeodesy and Remote Sensing
• LevelDoctoral dissertation (S3)
• Method typeMachine Learning
• Estimated build time± 7 working days
• ToolsTreeBagger, oobError, predict (Statistics and Machine Learning Toolbox)
• PriceUS$34 - US$219

Research goal

Classify land use class with a random forest and assess the importance of the features vegetation index and texture contrast.

Variables

Example result

• 0.11OOB error
• 0.944Test accuracy
• 0.942Macro F1
• 60Trees
• 5.231Importance Vegetation index
• 2.629Importance Texture contrast

A 60-tree random forest predicts the class of Land use class with an OOB error of 0.110 and test accuracy of 0.944 (macro F1 0.942); Vegetation index and Texture contrast prove far more important than the two noise predictors.

Computed from illustrative synthetic data, not from a real study.

About Random Forest (TreeBagger)

TreeBagger builds a random forest from many bootstrapped decision trees and gives out-of-bag error and variable importance. It suits tabular classification or regression with nonlinear relations and interactions.

Steps

1. 1Prepare features and labels, split into training and test sets
2. 2Train TreeBagger with hundreds of trees and OOBPredictorImportance
3. 3Monitor the out-of-bag error against the number of trees
4. 4Predict test data and compute accuracy and confusion matrix
5. 5Read variable importance (OOBPermutedPredictorDeltaError)
6. 6Plot OOB error and feature importance

Assumptions to check

• Training data represent the target population
• No target leakage into features
• Enough trees for the OOB error to stabilise

Reading and reporting the results

Report the OOB error, test accuracy, and the most important variables. Explain that importance reflects predictive contribution, not causation.

Example MATLAB code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

rng(7);
n = 500;
vegetation_index = randn(n, 1);
texture_contrast = randn(n, 1);
x3 = randn(n, 1);
X = [vegetation_index texture_contrast x3];
land_use_class = double(vegetation_index.^2 + texture_contrast + 0.3*randn(n, 1) > 0.8);

cv = cvpartition(land_use_class, 'HoldOut', 0.25);
Xtr = X(training(cv), :);  ytr = land_use_class(training(cv));
Xte = X(test(cv), :);      yte = land_use_class(test(cv));

b = TreeBagger(200, Xtr, ytr, 'Method', 'classification', ...
    'OOBPrediction', 'on', 'OOBPredictorImportance', 'on', 'MinLeafSize', 3);
pred = str2double(predict(b, Xte));
fprintf('OOB error = %.3f\n', oobError(b, 'Mode', 'ensemble'));
fprintf('Test accuracy = %.3f\n', mean(pred == yte));
disp(confusionmat(yte, pred));
disp(b.OOBPermutedPredictorDeltaError);

figure;
subplot(1,2,1); plot(oobError(b)); xlabel('Number of trees'); ylabel('OOB error');
subplot(1,2,2); bar(b.OOBPermutedPredictorDeltaError);
set(gca, 'XTickLabel', {'x1', 'x2', 'x3'}); ylabel('Importance');

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

• Payment: 50% down payment before work starts, balance after the deliverables are handed over
• Tax: Prices exclude 11% VAT
• Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
• Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions