Hyperspectral Image Classification Using Random Forest and Deep Learning Algorithms

Abstract

One of the purposes of hyperspectral remote sensing is to differentiate and identify the materials present on the Earth's surface by the spectral behavior of each object in the different regions of the electromagnetic spectrum. Such differentiation and identification can be accomplished through different image classification algorithms. However, there is no perfect classifier, since every algorithm has labeling errors. With the advent of orbital and aerial images of very high spatial and spectral resolution, the recognition of the materials present in urban environments is increasingly accurate. Thus, we thoroughly study different methodologies to identify the algorithm that presents the best results in the characterization of urban objects. The hyperspectral image used in the present study represents an area over Houston University - Texas and its surroundings, containing 48 spectral bands, with a spatial resolution of 1 meter and spectral range of 380 nm to 1050 nm. For the identification of 21 classes present in the study area, this paper analyzes two different classification methods: Deep Learning and Random Forest. To improve classification accuracy, performed the feature extraction. To obtain such preliminary results we used tools available in specific software as Normalized Difference Vegetation Index (NDVI), Minimum Noise Fraction (MNF), Principal Component Analysis (PCA) and Soil Adjusted Vegetation Index (SAVI). The image segmentation was performed using two different methods known as Multiresolution Segmentation and Spectral difference. Multiresolution segmentation needs parameters related to form and compactness. The best results were obtained with the values of form = 0.7 and compactness = 0.5, besides the scale of 10. From this, samples of all classes contained in the study area were selected for the training of the algorithms. This step is of paramount importance, as sample collection directly impacts the result of the classifications. After performing these steps, the information obtained from sample collection is entered into the data mining software (WEKA 3.8) to train the classification algorithms. The analysis of the results was performed by cross-validation, thus obtained the confusion matrix, calculated the Overall Accuracy (OA) and Kappa Index. The classification by the Random Forest method had an overall accuracy of 84.72% and a Kappa Index of 0.83. In turn, the Deep Learning algorithm had an overall accuracy of 81.32% and a Kappa index of 0.80. In this case, the classification by the Random Forest method presented better results for the hyperspectral image classification than the Deep Learning method. The accuracy difference obtained between the methods is not considered significant, so it is suggested for future work to analyze other complementary issues such as processing time.