Abstract
Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.
Highlights
Land use and land cover change (LUCC) has increasingly become a central component of global environmental change research [1,2]
Recent efforts to map land cover using remote sensing data have taken a variety of approaches, including visual interpretation classification [5], unsupervised clustering coupled with extensive ancillary data and manual labeling of clusters [6], supervised classification [7], expert system classification [8], artificial intelligence neural network classification [9], and decision tree classification [10,11]
For the purpose of paying equal attention to farmland protection and development maintenance, the region represents an ideal case study to evaluate semi-automatic land cover classification method applying in numbers of regions of China
Summary
Land use and land cover change (LUCC) has increasingly become a central component of global environmental change research [1,2]. Traditional unsupervised classification algorithms, such as maximum likelihood classification, use clustering techniques to identify spectrally distinct groups of data and are the earliest approach of land cover automatic classification that has employed pattern recognition techniques. The drawback of these algorithms is that the accuracy of land cover classification is not guaranteed and the land cover classifications are arbitrary. The result of land cover classification is influenced greatly by classification participants, and it is impossible to classify land cover automatically with these methods The algorithms such as neural network classification and fuzzy logic classification are highly complicated in their algorithm basis which makes them difficult to understand and apply widely. The main problem presented by decision tree classification is the construction of the decision tree and the assignment of thresholds for each sub nodes, which heavily depends on human experience and varies spatially and temporally
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