1 |
Abdel-Rahman, E. M., O. Mutanga, E. Adam, and R. Ismail, 2014. Detecting Sirex noctilio greyattacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers, ISPRS Journal of Photogrammetry and Remote Sensing, 88: 48-59.
DOI
|
2 |
Adam, E., O. Mutanga, J. Odindi, and E. M. Abdel-Rahman, 2014. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers, International Journal of Remote Sensing, 35(10): 3440-3458.
DOI
|
3 |
Adam, E. M., O. Mutanga, D. Rugege, and R. Ismail, 2012. Discriminating the papyrus vegetation (Cyperus papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP, International Journal of Remote Sensing, 33(2): 552-569.
DOI
|
4 |
Akar, O. and O. Gungor, 2013. Classification of multispectral images using Random Forest algorithm, Journal of Geodesy and Geoinformation, 1(2).
|
5 |
Archibald, R. and G. Fann, 2007. Feature selection and classification of hyperspectral images with support vector machines, IEEE Geoscience and Remote Sensing Letters, 4(4): 674-677.
DOI
|
6 |
Belgiu, M. and L. Dragut, 2016. Random forest in remote sensing: A review of applications and future directions, ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31.
DOI
|
7 |
Breiman, L., 2001. Random forests, Machine Learning, 45(1): 5-32.
DOI
|
8 |
Chan, J. C. W. and D. Paelinckx, 2008. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery, Remote Sensing of Environment, 112(6): 2999-3011.
DOI
|
9 |
Chutia, D., D. K. Bhattacharyya, K. K. Sarma, R. Kalita, and S. Sudhakar, 2016. Hyperspectral remote sensing classifications: a perspective survey, Transactions in GIS, 20(4): 463-490.
DOI
|
10 |
Dye, M., O. Mutanga, and R. Ismail, 2011. Examining the utility of random forest and AISA Eagle hyperspectral image data to predict Pinus patula age in KwaZulu-Natal, South Africa,Geocarto International, 26(4): 275-289.
DOI
|
11 |
Lawrence, R. L., S. D. Wood, and R. L. Sheley, 2006. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest), Remote Sensing of Environment, 100(3): 356-362.
DOI
|
12 |
Hao, P., Y. Zhan, L. Wang, Z. Niu, and M. Shakir, 2015. Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA, Remote Sensing, 7(5): 5347-5369.
DOI
|
13 |
Hsu, P. H., 2007. Feature extraction of hyperspectral images using wavelet and matching pursuit, ISPRS Journal of Photogrammetry and Remote Sensing, 62(2): 78-92.
DOI
|
14 |
Kim, H. O. and J. M. Yeom, 2014. Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multispectral satellite image data, International Journal of Remote Sensing, 35(19): 7046-7068.
|
15 |
Low, F., U. Michel, S. Dech, and C. Conrad, 2013. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using support vector machines, ISPRS Journal of Photogrammetry and Remote Sensing, 85: 102-119.
DOI
|
16 |
Mulla, D. J., 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps, Biosystems Engineering, 114(4): 358-371.
DOI
|
17 |
Ok, A. O., O. Akar, and O. Gungor, 2012. Evaluation of random forest method for agricultural crop classification, European Journal of Remote Sensing, 45(1): 421-432.
DOI
|
18 |
Pal, M., 2005. Random forest classifier for remote sensing classification, International Journal of Remote Sensing, 26(1): 217-222.
DOI
|
19 |
Pal, M. and G. M. Foody, 2010. Feature selection for classification of hyperspectral data by SVM, IEEE Transactions on Geoscience and Remote Sensing, 48(5): 2297-2307.
DOI
|
20 |
Sahoo, R. N., S. S. Ray, and K. R. Manjunath, 2015. Hyperspectral remote sensing of agriculture, Current Science, 108(5): 848-859.
|
21 |
Sonobe, R., H. Tani, X. Wang, N. Kobayashi, and H. Shimamura, 2014. Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data, Remote Sensing Letters, 5(2): 157-164.
DOI
|
22 |
Tatsumi, K., Y. Yamashiki, M. A. C. Torres, and C. L. R. Taipe, 2015. Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data, Computers and Electronics in Agriculture, 115: 171-179.
DOI
|
23 |
Thenkabail, P. S., E. A. Enclona, M. S. Ashton, and B. Van Der Meer, 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications, Remote Sensing of Environment, 91(3): 354-376.
DOI
|
24 |
Thenkabail, P. S. and J. G. Lyon (Eds.)., 2016. Hyperspectral remote sensing of vegetation, CRC Press, Boca Raton, Florida, US.
|
25 |
Waske, B., S. van der Linden, C. Oldenburg, B. Jakimow, A. Rabe, and P. Hostert, 2012. ImageRF-A user-oriented implementation for remote sensing image analysis with Random Forests, Environmental modelling & software, 35: 192-193.
DOI
|
26 |
Rural Development Administration (RDA), 2007. Schedule of farm work: Onion, Garlic, Wheat (e-book), http://www.nongsaro.go.kr, Accessed on Feb. 1, 2017.
|