Identification of Remote Sensing-Based Land Cover Types Combining Nearest-Neighbor Classification and SEaTH Algorithm

Abstract

The development of spaceborne remote sensing has greatly facilitated the land cover mapping at various spatial scales. Classification accuracy, however, is usually affected by the heterogeneous spectra of different land cover types for medium–low-spatial-resolution images. The study is aimed at improving the classification accuracy at a city scale by proposing a hierarchical classification method. Time-series Landsat-5 and Landsat-8 Operational Land Imager remote sensing images of 4 years were used as the classified images. A total of six first-class land cover types were determined, namely woodland, grassland, cropland, wetland, artificial surface and others. The object-based image analysis was chosen over pixel-based approaches. More specifically, the nearest-neighbor (NN) classification and SEparability and THresholds (SEaTH) algorithm were combined to produce a hierarchical classification method (NN-SEaTH). SEaTH algorithm was first used to extract the wetland after performing image segmentation in eCognition Developer. Then, the non-wetland was further classified to vegetation and non-vegetation by using a normalized difference vegetation index image. Finally, the other types were then obtained using the NN classification. To validate the proposed method, the NN classifier and NN-SEaTH method were compared. The proposed technique is shown to increase the overall accuracy (OA) and kappa coefficient (k) for the 4 years. The OA and k are, respectively, 96.46% and 0.9231, 96.63% and 0.9269, 96.88% and 0.9394, 95.22% and 0.9239 that are much larger than 88.13% and 0.7503, 88.83% and 0.7660, 88.64% and 0.7630, 87.33% and 0.7371 derived from the NN approach. The study provides a reference for medium-resolution-based land cover mapping by a hierarchical classification.