Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery

Abstract

Abstract:Pixel-based and object-based classifications are two commonly used approaches in extracting land cover information from remote sensing images. However, they each have their own inherent merits and limitations. This study, therefore, proposes a new classification method through the integration of pixel-based and object-based classifications (IPOC). Firstly, it employs pixel-based soft classification to obtain the class proportions of pixels to characterize the land cover details from pixel-scale properties. Secondly, it adopts area-to-point kriging to explore the class spatial dependence between objects for each pixel from object-based soft classification results. Thirdly, the class proportions of pixels and the class spatial dependence of pixels are fused as the class occurrence of pixels. Last, a linear optimization model on objects is built to determine the optimal class label of pixels within each object. Two remote sensing images are used to evaluate the effectiveness of IPOC. The experimental results demonstrate that IPOC performs better than the traditional pixel-based hard classification and object-based hard classification methods. Specifically, the overall accuracy of IPOC is 7.64% higher than that of pixel-based hard classification and 4.64% greater than that of object-based hard classification in the first experiment, while the overall accuracy improvements in the second experiment are 3.59% and 3.42%, respectively. Meanwhile, IPOC produces less salt and pepper effect than the pixel-based hard classification method and generates more accurate land cover details and small patches than the object-based hard classification method.

Assessing Cropland Area in West Africa for Agricultural Yield Analysis

“…GFSAD30 and GlobeLand30, which have shown better cropland estimation in Western Sahel, compared to the other land cover products, were developed using random forest and pixel-object-based (i.e., an optimization of the pixel-based and object-based methods) classification algorithms, respectively. These new algorithms are becoming popular within the remote sensing community, because of their abilities to accurately classify land cover [26,27]. However, at the country and regional scales of this West Africa analysis, using high quality and high density reference information, we found that both GFSAD30 and GlobeLand30 have a cropland class user's accuracy below that reported by the producers; which is in conformity with previous findings in assessing the GlobeLand30 dataset at a country level (Kenya) reported by the authors of [28].…”

Monitoring Land-Use/Land-Cover Changes at a Provincial Large Scale Using an Object-Oriented Technique and Medium-Resolution Remote-Sensing Images

“…More important, the accuracy of our results was relatively high. We attributed this high accuracy to the fact that OBIA not only uses spectral features of ground objects but also uses spatial features and textures, spatial structure features, shape, and other characteristics [1,10]. Thus, to a large extent, OBIA can overcome the negative effects of metameric substances with the same spectrum and a metameric spectrum with the same substance caused by using only spectrum features in traditional pixel-based methods to improve classification accuracy [11,12,22].…”

Improving Remote Sensing Image Super-Resolution Mapping Based on the Spatial Attraction Model by Utilizing the Pansharpening Technique

“…Experimental results show that the proposed SRMSAM-PAN can obtain a higher mapping accuracy than the existing SRMSAM methods.In recent years, many studies on SRM have been rapid developed. The Hopfield neural network [6,7], back-propagation neural network [8,9], object spatial dependence [10,11], indicator cokriging (ICK) [12,13], point spread function [14,15], and some super-resolution methods [16][17][18] have been successfully utilized in SRM. The above methods belong to soft-then-hard super-resolution mapping (STHSRM) types.…”