Detailed and precise information of land-use and land-cover (LULC) in rural area is essential for land-use planning, environment and energy management. The confusion in mapping residential and industrial areas brings problems in energy management, environmental management and sustainable land use development. However, they remain ambiguous in the former rural LULC mapping, and this insufficient supervision leads to inefficient land exploitation and a great waste of land resources. Hence, the extent and area of residential and industrial cover need to be revealed urgently. However, spectral and textural information is not sufficient for classification heterogeneity due to the similarity between different LULC types. Meanwhile, the contextual information about the relationship between a LULC feature and its surroundings still has potential in classification application. This paper attempts to discriminate settlement and industry area using landscape metrics. A feasible classification scheme integrating landscape metrics, chessboard segmentation and object-based image analysis (OBIA) is proposed. First LULC map is generated from GeoEye-1 image, which delineated distribution of different land-cover materials using traditional OBIA method with spectrum and texture information. Then, a chessboard segmentation of the whole LULC map is conducted to create landscape units in a uniform spatial area. Landscape characteristics in each square of chessboard are adopted in the classification algorithm subsequently. To analyze landscape unit scale effect, a variety of chessboard scales are tested, with overall accuracy ranging from 75% to 88%, and Kappa coefficient from 0.51 to 0.76. Optimal chessboard scale is obtained through accuracy assessment comparison. This classification scheme is then compared to two other approaches: a top-down hierarchical classification network using only spectral, textural and shape properties, and lacunarity based hierarchical classification. The distinction approach proposed is overwhelming by achieving the highest value in overall accuracy, Kappa coefficient and McNemar test. The results show that landscape properties from chessboard segment squares could provide valuable information in classification.