Y F Liu

Wuhan Uinv., School of Resource Environment Science, Wuhan, PRC.


Rural Land-use Automatic Interpretation Research Based on High-resolution RS Images

-------Taking Land-use classification of Yicheng City P.R. China as a case study

Y.F. Liu  FanXia  

(School Of Resource And Environmental Science, Wuhan University. Wuhan P.R. China)

The investigation of land use condition which is the basic application of RS technology having been researched for many years is also one of essential investigations about the economic and natural situation of a country. To protect and utilize the land resource of the nation, it needs to know the present land use condition authentically, exactly and timely. Due to the existing limitations on remote sensing image resolution and technology, the land use classification always only includes six types, which is cultivated land, grassland, forest, residential, water, unutilized land. In view of Nation land classification standard 2001, that is usually only specific to a secondary category, far from being able to meet the requirement of nation land use survey, which investigation is processing in more details. Meanwhile, the investigation of land use in rural area attaches more important on the recognition of cultivated land and some special residential types, such as bleachery land for grain, diversity types of garden land and some cultivate land and so on. Therefore, it is needed to put forward rural land use classification standards which suitable for rural especially.


In this study, some taste has been done to complete such task. Based on the characteristics of rural land use, a classification process is promoted which can meet the requirements of rural areas land use survey and be suitable for large-scale promotion at the same time. The classification system based on "National Land Classification," which mainly uses high-resolution remote sensing data (SPOT) concomitant with some compliancy supporting data such as landscape pattern indices(can treat surface features and its distribution as integration and synthesis which would help much to the classification). Moreover, the land cover classification may adopt the object-oriented image classification method which can be used to reduce the amount of calculation and increase the classification speed. The image segmentation method may be applying artificial ant colony algorithm or optimization. Then, one or two landscape pattern indices, which represent diversities of the differences surface features most, would be chosen as complementary classification indices adding remote sensing image classification. Lastly, based on above theoretical research, a sample district of Yicheng city would be chosen as the demonstration site for the case study.


By using this classification process, it can provide land use map automatically with more details, which meets the requirements of the National Land Survey directly and can certain aid nation economic and developing policy making much.

Key words: RS, Landscape pattern indices, ant colony algorithm, object-oriented image classification