Accuracy assessment of Land Use Classification using hybrid methods

Hillside region accounts for 73.6% of the land in Taiwan. The mountain region consists of high mountain valley of deep and faults-knit environment, fragile geological, abrupt slopes, and steep rivers. With the rapid development in recent years, there has been not only great change in land use, but the destruction of the natural environment, the improper use of soil and water resources also. It is prudent to effectively build and renew the existing land use information as soon as possible. Among various land use status investigation and monitoring technology, the remote sensing has the advantages in getting data covering wide-range and in richness of spectral and spatial information. In this study, hybrid land use classification methods combining with an edge-based segmentation and three kinds of supervised classification methods, means Maximum Likelihood, Decision Tree, and Support Vector Machine, were conducted to automatically recognize land use types for Yi-Lan area using multi-resource data, e. g. satellite images and DTM. The second land use investigation result of Taiwan in 2006 by the Ministry of the Interior is assumed as the ground truth. The higher classification accuracy results indicate that the proposed methods can be used to automatic classify agricultural and forest land use types. Moreover, the results of object-based DT and object-based SVM are better than the ones for the object-based ML methods. However, adequate training is not easy to select the appropriate samples for the transportation, hydrology, and built-up land classes.