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This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm (GOA) and the multiple-class Neural network (MNN) for urban pattern detection in Hanoi, Vietnam. Four bands of SPOT 7 image and derivable NDVI, NDWI were used to generate image segments with associated attributes by PCI Geomatics software. These segments were classified into four urban surface types (namely water, impervious surface, vegetation and bare soil) by the proposed model. Alternatively, three training and validation datasets of different sizes were used to verify the robustness of this model. For all tests, the overall accuracies of the classification were approximately 87%, and the Area under Receiver Operating Characteristic curves for each land cover type was 0.97. Also, the performance of this model was examined by comparing several statistical indicators …