Hybrid model to optimize object-based land cover classification by meta-heuristic algorithm: an example for supporting urban management in Ha Noi, Viet Nam

Abstract

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 with common benchmark classifiers. The results showed that GMNN out-performed established methods in all comparable indicators. These results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover studies. In a broader sense, classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.

Keywords

• urban
• remote sensing
• object-based classification
• neural network
• grasshopper optimization algorithm