Accuracy Improvements to Pixel-Based and Object-Based LULC Classification with Auxiliary Datasets from Google Earth Engine

Abstract

The monitoring and assessment of land use/land cover (LULC) change over large areas are significantly important in numerous research areas, such as natural resource protection, sustainable development, and climate change. However, accurately extracting LULC only using the spectral features of satellite images is difficult owing to landscape heterogeneities over large areas. To improve the accuracy of LULC classification, numerous studies have introduced other auxiliary features to the classification model. The Google Earth Engine (GEE) not only provides powerful computing capabilities, but also provides a large amount of remote sensing data and various auxiliary datasets. However, the different effects of various auxiliary datasets in the GEE on the improvement of the LULC classification accuracy need to be elucidated along with methods that can optimize combinations of auxiliary datasets for pixel- and object-based classification. Herein, we comprehensively analyze the performance of different auxiliary features in improving the accuracy of pixel- and object-based LULC classification models with medium resolution. We select the Yangtze River Delta in China as the study area and Landsat-8 OLI data as the main dataset. Six types of features, including spectral features, remote sensing multi-indices, topographic features, soil features, distance to the water source, and phenological features, are derived from auxiliary open-source datasets in GEE. We then examine the effect of auxiliary datasets on the improvement of the accuracy of seven pixels-based and seven object-based random forest classification models. The results show that regardless of the types of auxiliary features, the overall accuracy of the classification can be improved. The results further show that the object-based classification achieves higher overall accuracy compared to that obtained by the pixel-based classification. The best overall accuracy from the pixel-based (object-based) classification model is 94.20% (96.01%). The topographic features play the most important role in improving the overall accuracy of classification in the pixel- and object-based models comprising all features. Although a higher accuracy is achieved when the object-based method is used with only spectral data, small objects on the ground cannot be monitored. However, combined with many types of auxiliary features, the object-based method can identify small objects while also achieving greater accuracy. Thus, when applying object-based classification models to mid-resolution remote sensing images, different types of auxiliary features are required. Our research results improve the accuracy of LULC classification in the Yangtze River Delta and further provide a benchmark for other regions with large landscape heterogeneity.

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

“…The relative superiority of the object-based approach has also been shown when applied to land classification [180,181], such as bamboo mapping [182] or tree classification [183,184]. Nonetheless, even if the object-based methods are generally more accurate, they remain harder to set up because they need to perform a segmentation step (to create the objects) before the classification.…”

ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

“…In addition to studies that have prepared LULC maps at the global or European scales, a wide range of articles have studied classification methods from pixel-and object-based points of view. Various pixel-based and object-based ML methods [58], semi-automated and automated classification techniques [59], along with different features retrieved from EOs [60], were utilized to generate LULC maps. For instance, Verde et al [48] developed a classification workflow for fine-scale object-based land cover mapping for Greek terrestrial territory by evaluating several classification techniques and strategies for automatic and manual training data extraction.…”

“…They found that Sentinel-2 Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Sentinel-1 VV measurements were the most relevant features to optimize the SVM classifier and achieve higher classification accuracy. In this way, Qu et al [58] investigated the performance of various auxiliary features in improving the accuracy of seven pixel-based and seven object-based RF classification models. Among all RF classification models analyzed by Qu et al [58], the object-based methods showed higher overall accuracy than the pixel-based classification methods, as the best overall accuracy was achieved when the object-based method was used with spectral data only.…”

A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery

“…Li et al [26] carried out fine LULC classification in open-pit mining areas, revealing that MLAs can provide improved classification performance. Qu et al [30] improved the classification accuracy by extracting the soil characteristics and phenological characteristics of the auxiliary dataset from the Google Earth Engine. Yao et al [31] studied the advantages of using continuous multi-angle remote sensing data for classification, attempting to make use of the complementarity of multiangle information.…”