## 3 Experimental Results and Discussion
### Experiment on Data Set 1  
In this experiment, a portion of a 0.61-m resolution QuickBird (512 \(\times\) 512 pixels) image including three multispectral bands (red, green, and blue), which was obtained on August, 2005 (Figure 2a), is used. The study area locates in an urban area in Xuzhou City, China. Figure 2b shows the reference image. Table 1 describes the training and testing samples in the experiment 1. From Figure 2a, we can find the distributions of road and building are irregular and complex, the spectral similarity between road and building is high, and the same situation for water and shadow, grass and tree, etc., it is difficult to classify the image only using spectral information.  
Figure 2: _Cont._  
Figure 3a gives the OA curves obtained with different window sizes for MV, GS, and CAMRF-FLI algorithms tested in Experiment 1, respectively. They have similar OA with small window size, while CAMRF-FLI provides a significant advantage over MV and GS in terms of OA with the increase of window size. MV and GS obtain the highest OA with a 5 \(\times\) 5 window, and CAMRF-FLI has the highest OA with a 15 \(\times\) 15 window. Figure 2c-h shows the classification results deriving from the MLC, MV (5 \(\times\) 5), MRF, OBV, GS (5 \(\times\) 5), and CAMRF-FLI (15 \(\times\) 15), respectively. As shown in Figure 2c, because many mixed or spectral similar pixels exist, MLC shows a map with \"salt and pepper\" without incorporating spatial contextual information. Figure 2d-h all give more homogenous regions than MLC, but GS and CAMRF-FLI are more effective in eliminating isolated pixels. GS removes almost of the isolated pixels and obtains satisfactory results, but oversmooth results occur on boundaries and producing large patches. In the CAMRF-FLI results, most of the isolated pixels are eliminated and image details are satisfactorily remained. Seen from Table 1, CAMRF-FLI produces the highest OA and most of the best Producer's Accuracies. The OA gains of CAMRF-FLI over MLC, MV, MRF, OBV, and GS are 10.81%, 6.99%, 5.49%, 6.44%, and 4.45%, respectively; the classification accuracies are statistically significant at the 5% level of significance.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Class** & \begin{tabular}{c} **Training** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **Testing** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **MLC** \\ **(5 \(\times\) 5)** \\ \end{tabular} & \begin{tabular}{c} **MV** \\ **(5 \(\times\) 5)** \\ \end{tabular} & \begin{tabular}{c} **MRF** \\ **(5 \(\times\) 5)** \\ \end{tabular} & \
\begin{tabular}{c} **OBV** \\ **(5 \(\times\) 5)** \\ \end{tabular} &
\begin{tabular}{c} **GS** \\ **(15 \(\times\) 15)** \\ \end{tabular} \\ \hline Water & 2010 & 8042 & 91.93\% & 96.53\% & 95.47\% & 97.28\% & 96.12\% & 95.93\% \\ Grass & 1047 & 4187 & 78.43\% & 81.01\% & 77.02\% & 79.27\% & 76.84\% & 81.59\% \\ Tree & 1355 & 5421 & 53.22\% & 53.02\% & 68.48\% & 62.39\% & 72.20\% & 93.65\% \\ Building & 7846 & 31,383 & 52.52\% & 55.51\% & 57.74\% & 54.50\% & 58.76\% & 91.13\% \\ Road & 1863 & 7454 & 86.73\% & 91.89\% & 89.70\% & 92.54\% & 90.30\% & 86.09\% \\ Bare soil & 1130 & 4522 & 80.09\% & 86.15\% & 87.78\% & 90.15\% & 89.34\% & 98.41\% \\ Shadow & 1134 & 4536 & 70.84\% & 82.95\% & 88.60\% & 96.13\% & 89.75\% & 96.19\% \\ \hline OA & & & 80.79\% & 84.61\% & 86.11\% & 85.16\% & 87.15\% & 91.60\% \\ \(\kappa\) & & & 0.7494 & 0.7949 & 0.8145 & 0.8028 & 0.8278 & 0.8852 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of samples, producer’s accuracy, OA, and \(\kappa\) of the classification in experiment 1.  
Figure 2: Dataset and classification results in Experiment 1. (**a**) QuickBird image of Xuzhou RGB (red, green, blue), center coordinates: [34\({}^{\circ}\)10\({}^{\prime}\)18.61\({}^{\prime\prime}\) N, 117\({}^{\circ}\)12\({}^{\prime}\)23.44\({}^{\prime\prime}\) E]. (**b**) Reference data. (**c**–**h**) is the classification result using MLC, MV (5 \(\times\) 5), MRF, OBV, GS (5 \(\times\) 5), and CAMRF-FLI (15 \(\times\) 15), respectively.