# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 4 Experiment Results
### Experimental Setup  
We carried out experiments on four dataset (three multispectral (MS) datasets acquired with the WorldView-2, IKONOS (the name comes from the Greek word _eik\(\bar{o}\)n_) [45], and Quickbird sensors and one hyperspectral (HS) dataset acquired with the Reflective Optics System Imaging sensors (ROSIS)). A brief description of these datasets is provided below.  
Figure 2: Spatial feature transforms (SFT) used in SA-PNN.  
* The WorldView-2 Washington dataset [46] comprises a high-resolution PAN image with a spectral range from 450 nm to 800 nm and an MS image with eight bands, including four standard colors (red, green, blue, and near-infrared) (1) and four new bands (coastal, yellow, red edge, and near-infrared) (2). The dimension of the PAN image is 24,800 \(\times\) 23,600, whereas the size of the MS image is 6200 \(\times\) 5900. The spatial resolution of the PAN image is 0.46 m and that of the MS image is 1.84 m so that the resolution ratio \(R\) is 4. The radiometric resolution is 11 bits.
* The IKONOS Hobart dataset [47] contains a PAN image covering the spectral range from 450 nm to 900 nm and an MS image with four bands (blue, green, red, and near infrared). The dimension of the PAN image is 13,148 \(\times\) 12,124 whereas the size of the MS image is 3287 \(\times\) 3031. The resolution ratio \(R\) is equal to 4 and the radiometric resolution is 11 bit, with the spatial resolution being 4 m for the MS image and 1 m for the PAN image.
* The Quickbird Sundarbans dataset [48] was obtained by the QuickBird sensor which provides a high-resolution PAN image with spatial resolution of 0.6 m and a four-band (blue, green, red, and near infrared) MS image with spatial resolution of 2.4 m. The dimension of the PAN image is 15,200 \(\times\) 15,200 while that of the MS image is 3800 \(\times\) 3800. The spectral range of the PAN image is from 450 nm to 900 nm. The resolution ratio \(R\) is equal to 4, and the radiometric resolution is 11 bit.
* The Pavia University dataset [49] comprises a 103-band HS image covering a spectral range from 400 nm to 2500 nm, with spatial resolution of 1.3 m. The dimension of the HS image is 610 \(\times\) 340. Since the PAN image is absent, it is simulated by averaging the visible spectral bands of the HS image. The resolution ratio \(R\) for this dataset is usually manually set to 5 following the consensus in the pansharpening task, which means that the low-spatial-resolution HS image would be generated by degrading the original HS image with the scale factor of 5 in the subsequent experiments. The radiometric resolution is 13 bit.  
We conduct experiments in the case of reduced-resolution assessments according to [PERSON]'s protocol [50]. Specifically, the LR-MBO image degraded from the original MBO image will interpolate to the same spatial size as the degraded PAN image using a polynomial kernel (EXP) [51]. Subsequently, both of the pre-interpolated LR-MBO and degraded PAN image are used as the input of network, with the original MBO image as the ground truth. To demonstrate the superiority of the proposed SA-PNN model, several state-of-the-art CNN-based pansharpening methods are considered in our experiments for comparison, including PNN [39], DRPNN [40], and DiCNN1 [41]. In addition, several representative traditional methods, including PCA [11], GSA [16], PRACS [19], ATWT [23], Generalized Laplacian Pyramid with High-Pass Filtering (MTF-GLP-HPM) [26], CNMF [28], smoothing filter-based intensity modulation (SFIM) [52], guided filter PCA (GFPCA) [53], and Bayesian Sparse [54], are also run for comparison. Several widespread full-reference performance measures are used to evaluate pansharpening quality: average universal image quality index (Q) [55], x-band extension of universal image quality index (Qx) [56], spectral angle mapper (SAM) [57], _Erreur Relative Globale Adimensionnelle de Synthese_ (ERGAS) [58], root mean squared error (RMSE), and spatial correlation coefficient (SCC) [59].