Object-Based Automatic Mapping of Winter Wheat Based on Temporal Phenology Patterns Derived from Multitemporal Sentinel-1 and Sentinel-2 Imagery

Abstract

Although winter wheat has been mapped by remote sensing in several studies, such mapping efforts did not sufficiently utilize contextual information to reduce the noise and still depended heavily on optical imagery and exhausting classification approaches. Furthermore, the influence of similarity measures on winter wheat identification remains unclear. To overcome these limitations, this study developed an object-based automatic approach to map winter wheat using multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. First, after S1 and S2 images were preprocessed, the Simple Non-Iterative Clustering (SNIC) algorithm was used to conduct image segmentation to obtain homogeneous spatial objects with a fusion of S1 and S2 bands. Second, the temporal phenology patterns (TPP) of winter wheat and other typical land covers were derived from object-level S1 and S2 imagery based on the collected ground truth samples, and two improved distance measures (i.e., a composite of Euclidean distance and Spectral Angle Distance, (ESD) and the difference–similarity factor distance (DSF)) were built to evaluate the similarity between two TPPs. Third, winter wheat objects were automatically identified from the segmented spatial objects by the maximum between-class variance method (OTSU) with distance measures based on the unique TPP of winter wheat. According to ground truth data, the DSF measure was superior to other distance measures in winter wheat mapping, since it achieved the best overall accuracy (OA), best kappa coefficient (Kappa) and more spatial details for each feasible band (i.e., NDVI, VV, and VH/VV), or it obtained results comparable to those for the best one (e.g., NDVI + VV). The resultant winter wheat maps derived from the NDVI band with the DSF measure achieved the best accuracy and more details, and had an average OA and Kappa of 92% and 84%, respectively. The VV polarization with the DSF measure produced the second best winter wheat maps with an average OA and Kappa of 91% and 80%, respectively. The results indicate the great potential of the proposed object-based approach for automatic winter wheat mapping for both optical and Synthetic Aperture Radar (SAR) imagery.

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments

“…The Otsu algorithm was proposed by OTSU N in 1979 [ 40 ]. It is mainly applied to digital image segmentation [ 41 , 42 ], and its main idea is to divide the image into foreground and background parts by using a threshold [ 43 , 44 , 45 ], to maximize the variance between foreground and background. Suppose the segmentation threshold is , the proportion of foreground pixels is , the average gray level is , the proportion of background pixels is , the average gray level is , the total average gray level of the image is , and the variance between-cluster is , then: …”

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI

“…After selecting the best features combination, they successfully used the random forest classifier to extract and map the rice in the Dongting Lake region of China with the OA of 95%. Wang Limei et al (2022) [ 27 ] proposed an object-based method for automatic identification of winter wheat based on the fusion of time series Sentinel-1 and Sentinel-2 data. The OA reached 92%, and the Kappa coefficient was 0.84.…”

“…Obviously, in the application of combining SAR and optical features for ground extraction [25][26][27], the most methods input features into classifiers and select samples for supervised classification. For information such as texture features derived from single SAR image, the discriminative capability is usually limited by the resolution and noise constraints of the original image, and it takes a long time to input the features into some typical machine learning classifiers such as RF and KNN.…”

Extracting winter wheat based on multi-feature optimization of short-time series synthetic aperture radar data with dual polarizations

“…Its cultivation area, spatial distribution, growth, and yield are of great significance to the development of the national economy, the structure of crop cultivation, and the adjustment of agricultural policy. [4][5][6] The traditional methods for obtaining and updating information about crop area and distribution primarily involve referencing data, analyzing official statistics, and conducting field investigations, which consume a lot of time and financial resources. 7 Remote sensing technology is characterized by broadness, timeliness, economy, and comprehensiveness, which can quickly and accurately obtain the planting area and distribution of crops.…”