Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery

Abstract

Geographic object-based image analysis (GEOBIA) has been widely used in the remote sensing of agricultural crops. However, issues related to image segmentation, data redundancy and performance of different classification algorithms with GEOBIA have not been properly addressed in previous studies, thereby compromising the accuracy of subsequent thematic products. It is in this regard that the current study investigates the optimal scale parameter (SP) in multi-resolution segmentation, feature subset, and classification algorithm for use in GEOBIA based on multisource satellite imagery. For this purpose, a novel supervised optimal SP selection method was proposed based on information gain ratio, and was then compared with a preexisting unsupervised optimal SP selection method. Additionally, the recursive feature elimination (RFE) and enhanced RFE (EnRFE) algorithms were modified to generate an improved EnRFE (iEnRFE) algorithm, which was then compared with its precursors in the selection of optimal classification features. Based on the above, random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM) were applied to segmented objects for crop classification. The results indicated that the supervised optimal SP selection method is more suitable for application in heterogeneous land cover, whereas the unsupervised method proved more efficient as it does not require reference segmentation objects. The proposed iEnRFE method outperformed the preexisting EnRFE and RFE methods in optimal feature subset selection as it recorded the highest accuracy and less processing time. The RF, GBDT, and SVM algorithms achieved overall classification accuracies of 91.8%, 92.4%, and 90.5%, respectively. GBDT and RF recorded higher classification accuracies and utilized much less computational time than SVM and are, therefore, considered more suitable for crop classification requiring large numbers of image features. These results have shown that the proposed object-based crop classification scheme could provide a valuable reference for relevant applications of GEOBIA in crop recognition using multisource satellite imagery.

Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes

“…An example of this can be seen in Figure 12c, where the supervised approach generated segments with well-defined boundaries and a better geometric match to the LPIS parcels than the two unsupervised Bayesian approaches in Figure 12a,b, respectively. The adaptability of supervised segmentation optimization was also asserted by Yang et al [39] after testing a supervised optimization approach based on the information gain ratio and an unsupervised optimization approach based on MI and WV as was proposed by Espindola et al [33]. The major defect of any supervised optimization method is the reliance on reference data, which are tedious to obtain [29].…”

“…For the first approach, which we termed default optimization, we optimized the scale parameter while keeping the shape and compactness parameters constant at their default, as is mostly done in the literature [20,31,[37][38][39][43][44][45]. Shape was kept at 0.1, and compactness was kept at 0.5.…”

“…A comparative analysis by Grybas et al [36] showed that the objective function outperformed the ESP tool. Various variations [19,[37][38][39][40][41][42] of the objective function have been used in the literature.…”

“…We compared the Böck and AD metrics by separately using each of them in two unsupervised optimization approaches to optimize the parameters of the MRS algorithm to delineate agricultural parcels from 21 Sentinel-2 images of 10 × 10 km sizes in Lower Saxony, Germany. In the first optimization approach, as is mostly done in the literature [20,31,[37][38][39][43][44][45], we optimized scale while keeping the shape and compactness parameters constant at their default values. In the second optimization approach, we employed Bayesian optimization to optimize all three MRS parameters.…”

Introducing GEOBIA to Landscape Imageability Assessment: A Multi-Temporal Case Study of the Nature Reserve “Kózki”, Poland

“…The automated scale parameter approach, as an unsupervised method of segmentation accuracy assessment [42][43][44], also provides a more objective basis on which to set SP, a key factor for image object classification accuracy [45,46]. To further improve the process of selecting optimal SP values, an enhancement to ESP software was released as ESP2, an e-Cognition plug-in [36].…”