*4.5. Classification Accuracy Verification*

In order to verify the application accuracy of the fusion results in land use classification, we selected an area containing mountains, buildings, and waters for research. First, the three methods are applied to different land use types for spatiotemporal integration. Secondly, the fusion results are divided into forest land, dry land, construction land, and water through the supervised classification method. Finally, through the comparison with the data of the Third National Land Survey of China (TNLS), it can be seen from Figure 5 and Table 5 that the classification results of the fused images (Figure 5a) are highly consistent with the data of the TNLS of China (Figure 5b). However, compared with the data of TNLS, the classification results did not effectively divide the small area of water around the construction land region, which made the water area in the classification results smaller. For forest land and dry land, the classification results are scattered. At the same time, due to the time difference between the fusion image and TNLS, the original dry land is distributed with crops, which makes the classification result of the fusion image become forest land. This may result in the increase of forest land area and the decrease of dry land area. For construction land, the area may be increased due to construction activities. However, the classification results are basically consistent with the data of TNLS. In general, the classification result of the fusion image is good, and the difference of land area of each type is within 15%. Therefore, images fused by spatiotemporal fusion model can be used for land use classification.

**Figure 5.** (**a**) Supervise classification results; (**b**) Data of China's Third National Land Survey.


**Table 5.** Comparison between classification results and TNLS data.
