## 3 Experimental Results and Discussion
### Experiment on Data Set 2  
In this experiment, a QuickBird image (512 \(\times\) 512 pixels) including three 0.61-m resolution fused multispectral bands (red, green, and blue) is applied. It locates the suburb area of Xuzhou, China, which was obtained on August, 2005 (Figure 4a). As shown in Figure 4a, the spectral information between road and building is very similar, and the same situation for water and shadow, grass, tree, etc. The road and building staggers distribution, thus, it is difficult to classify the image with spectral information alone. Figure 4b gives the reference image. Table 2 shows the training and testing samples in the experiment 2. As shown in Figure 3b, MV, GS, and CAMRF-FLI algorithms also have similar OA with small window size, but CAMRF-FLI shows more robust to the window size than MV and GS in terms of OA. Seen from Figure 3b, MV and GS obtain the highest OA at the 5 \(\times\) 5 window, and CAMRF-FLI achieves the best OA at the 13 \(\times\) 13 window. Figure 4c-h shows the classification results provided by MLC, MV (5 \(\times\) 5), MRF, OBV, GS (5 \(\times\) 5), and CAMRF-FLI (13 \(\times\) 13) algorithms, respectively. In Figure 4c, for the spectral similarity, MLC obtains a map with \"salt and pepper\" noise without incorporating spatial information, road and building and bare soil, tree and grass, and water and shadow are confused in the classification result. Figure 4d-h all obtain more homogenous regions than Figure 4c for incorporating spatial information, while MV, MRF, OBV, and GS show weaker performance than CAMRF-FLI. CAMRF-FLI produces homogeneous areas while preserving more image details. Table 2 gives the quantitative results. In Table 2, MV, MRF, OBV, GS, and CAMRF-FLI achieve higher classification accuracies than MLC. Among them, CAMRF-FLI obtains the highest OA and most of the best Producer's Accuracies. In terms of OA, CAMRF-FLI achieves a value of 79.75%, with gain of 12.89%, 9.52%, 7.35%, 8.01%, and 6.09% over other algorithms, respectively. According to the results of [PERSON]'s test, all classification accuracies are statistically significant at the 5% level of significance. Figure 3: (**a,b**) shows the OA obtained with different window sizes for MV, GS, and CAMRF-FLI algorithms tested in experiments 1 and 2, respectively. Figure 4: Dataset and classification results in Experiment 2. (**a**) QuickBird image of Xuzhou RGB (red, green, blue), center coordinates: [34\({}^{\circ}\)12\({}^{\prime}\)45.80\({}^{\prime\prime}\) N, 117\({}^{\circ}\)05\({}^{\prime}\)6.94\({}^{\prime\prime}\) 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 (13 \(\times\) 13), respectively. Based on quantitative and qualitative assessments, CAMRF-FLI achieves the best performance for most classes. The reason for this may be that MLC utilizes spectral alone in the classification, thus producing poor results for mixed or spectral similar pixels. For the other methods, MV simply assigns the majority class in a sliding window to the centered pixel without considering different effects on the central pixel from its neighborhood, as a result, high OA is obtained at the cost of oversmooth. The result of OBV mainly depends on the performance of segmentation, while a fine segment result is difficult to obtain, usually producing large patches and removing details. In GS, Gaussian distance function is used to describe different weights on the centered pixel, but the class spatial dependency information between neighboring pixels is not considered, and also neglecting the centered pixel's own features. Thus, it may produce oversmoothed results. MRF applies the spatial regularization by utilizing neighbor label information to produce homogeneous areas, while neighboring pixels' influences on the centered pixel are treated equally, \(\beta\) is fixed for all neighbor windows across the whole image, and only 8-neighborhood is considered.