# Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
## 4 Experimental Results  
In our experiments, three real HSI data sets are used to evaluate the proposed MSuperLPP. The first one is the Indian Pines data set, which was acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over northwestern Indiana, including 220 spectral bands as well as 145 \(\times\) 145 pixels, and its available ground-truth data contains 10249 pixels with 16 classes. 40 noisy bands are removed, and the remaining 180 bands are used for experiments. The second one is the Zaoyuan data  
Figure 1: A flowchart of the proposed MSuperLPP for hyperspectral image (HSI) classification. set, which was collected by the Operational Modular Imaging Spectrometer (OMIS) over the Zaoyuan region, China, in 2001. The data set comprises \(137\times 202\) pixels and 128 bands covering the region. After removing the noisy bands, 80 bands remain. Moreover, 23821 pixels with 8 classes are used for classification. The last one is the Salinas data set, which was collected by AVIRIS over Salinas Valley, California. The scene used for the experiments comprises \(250\times 110\) pixels and 224 bands and has 14 testing classes. After removing the noisy bands, 174 bands are Left for the experiments. For the Indian Pines and Zaoyuan data sets, we randomly choose 5, 10, 30, and 50 pixels per class for training while the other pixels are used for test. Since some classes have a few samples, we select at most half of the total samples. For the Salinas data set, 1, 3, 5, and 10 pixels per class are selected as training samples. After a dimensionality reduction, both the nearest neighbor (NN) classifier and the support vector machine (SVM) with the radial basis function (RBF) kernel are subsequently applied to evaluate the proposed method. The free parameters of the two classifiers are set with cross-validation [38], including the number of nearest neighbors for the NN classifier and the RBF scale and the regularization coefficient for the SVM classifier. We repeat all the classification experiments ten times to avoid random bias, and the average accuracies are presented. We compare the proposed MSuperLPP with GlobalLPP-SF (global LPP on spectral feature), GlobalLPP-SSCF (global LPP on region convariance descriptor based spectral-spatial covariance feature), SuperLPP-SF (superpixelwise LPP on spectral feature), and SuperLPP-SSCF (superpixelwise LPP on spectral-spatial covariance feature). In our experimental settings, some other parameters are empirically determined as follows. The window's size of region covariance descriptor is \(3\times 3\) for GlobalLPP-SSCF, SuperLPP-SSCF, and MSuperLPP. In MSuperLPP, the fundamental number of superpixel \(N_{sf}\) is set to 100 for Indian Pines, 40 for Zaoyuan, and 15 for Salinas, while \(P\) is set to 4 for all three data sets. Tables 1-3 demonstrate the overall classification accuracies on the Indian Pines, Zaoyuan, and Salinas data sets, respectively. Figure 2 shows the influence of the number of training samples on the classification performance. As observed, GlobalLPP-SSCF performs better than GlobalLPP-SF while SuperLPP-SSCF is better than SuperLPP-SF, which suggests that a spectral-spatial covariance feature is beneficial to improving classification accuracy. Meanwhile, SuperLPP-SSCF and SuperLPP-SF achieve higher accuracies than GlobalLPP-SSCF and GlobalLPP-SF, respectively, which indicates that a superpixel segmentation can enhance classification accuracy. Among all the five considered methods, our proposed MSuperLPP yields the highest accuracies. For visual inspection purposes, the classification maps obtained with five compared methods are given in Figures 3-5. Here, we only show the results obtained with the nearest neighbor classifier when the size of the training set is set to 50 for Indian Pines and Zaoyuan and 10 for Salinas, respectively. It can be seen that our MSuperLPP yields the best regional consistency and agrees more with the ground truth.