Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

Zhang, Rui; Tang, Zhanzhong; Luo, Dong; Luo, Hongxia; You, Suya; Zhang, Tao

doi:10.3390/app11156923

Cited by 8 publications

(3 citation statements)

References 29 publications

(30 reference statements)

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“…However, it is tricky to determine which source has the more reliable classification result and set a strategy for merging the two classification results in the decision-level fusion. There are also some studies that conducted SAR and optical data fusion for image classification or target recognition in cloudy areas, and some have reported an improvement in accuracy compared with using single optical data [15,[25][26][27][28][29]. For example, Zhang et al proposed to combine optical and SAR data for classification in cloudy areas [15], Sun et al proposed a crop classification method based on optical and SAR response mechanism in cloudy and rainy areas [25], Yang et al proposed an object-based classification method for cloudy coastal areas using optical and SAR images for vulnerability assessment of marine disaster [27], and Sharesha et al combined topographic parameters and SAR data to solve the problem of cloud cover in classification [28].…”

Section: Introductionmentioning

confidence: 99%

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

2021

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Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable.

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Section: Introductionmentioning

confidence: 99%

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

2021

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“…Constructing crop-sensitive time series vegetation indices can amplify differences to facilitate the identification tasks [6]. However, the identification process is usually difficult because of the complexity of agricultural farming patterns and the limited amount of remote sensing data, due to overcast weather [7][8][9]. The effective use of spatial features is a promising idea for crop classification in such challenging areas.…”

Section: Introductionmentioning

confidence: 99%

Extracting Citrus-Growing Regions by Multiscale UNet Using Sentinel-2 Satellite Imagery

Li,

Liu,

et al. 2023

Remote Sensing

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Citrus is an important commercial crop in many areas. The management and planning of citrus growing can be supported by timely and efficient monitoring of citrus-growing regions. Their complex planting structure and the weather are likely to cause problems for extracting citrus-growing regions from remote sensing images. To accurately extract citrus-growing regions, deep learning is employed, because it has a strong feature representation ability and can obtain rich semantic information. A novel model for extracting citrus-growing regions by UNet that incorporates an image pyramid structure is proposed on the basis of the Sentinel-2 satellite imagery. A pyramid-structured encoder, a decoder, and multiscale skip connections are the three main components of the model. Additionally, atrous spatial pyramid pooling is used to prevent information loss and improve the ability to learn spatial features. The experimental results show that the proposed model has the best performance, with the precision, the intersection over union, the recall, and the F1-score reaching 88.96%, 73.22%, 80.55%, and 84.54%, respectively. The extracted citrus-growing regions have regular boundaries and complete parcels. Furthermore, the proposed model has greater overall accuracy, kappa, producer accuracy, and user accuracy than the object-oriented random forest algorithm that is widely applied in various fields. Overall, the proposed method shows a better generalization ability, higher robustness, greater accuracy, and less fragmented extraction results. This research can support the rapid and accurate mapping of large-scale citrus-growing regions.

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“…For this reason, to control the non-grain-oriented use of agricultural land and to guarantee national food security, monitoring of the area of agricultural crop planting is of utmost importance [3]. Moreover, remote-sensing technology has unique advantages in terms of agricultural crop monitoring due to its large range of observation and short cycles [4], and many studies have utilized remote-sensing technology to quickly extract the spatial distributions of agricultural crops [5]. At present, the commonly used methods for crop mapping by remote sensing are pixel-based classification and object-oriented classification; of these, the latter has been gradually adopted in more studies compared to the former [6,7] because it can avoid misclassification caused by certain pixels of the same object having different spectra or different objects having the same spectra [8], and can effectively prevent salt-and-pepper noise [9].…”

Section: Introductionmentioning

confidence: 99%

Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

Zhang²,

Luo

et al. 2022

Remote Sensing

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In recent years, the scale of rural land transfer has gradually expanded, and the phenomenon of non-grain-oriented cultivated land has emerged. Obtaining crop planting information is of the utmost importance to guaranteeing national food security; however, the acquisition of the spatial distribution of crops in large-scale areas often has the disadvantages of excessive calculation and low accuracy. Therefore, the IO-Growth method, which takes the growth stage every 10 days as the index and combines the spectral features of crops to refine the effective interval of conventional wavebands for object-oriented classification, was proposed. The results were as follows: (1) the IO-Growth method obtained classification results with an overall accuracy and F1 score of 0.92, and both values increased by 6.98% compared to the method applied without growth stages; (2) the IO-Growth method reduced 288 features to only 5 features, namely Sentinel-2: Red Edge1, normalized difference vegetation index, Red, short-wave infrared2, and Aerosols, on the 261st to 270th days, which greatly improved the utilization rate of the wavebands; (3) the rise of geographic data processing platforms makes it simple to complete computations with massive data in a short time. The results showed that the IO-Growth method is suitable for large-scale vegetation mapping.

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Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

Cited by 8 publications

References 29 publications

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

Extracting Citrus-Growing Regions by Multiscale UNet Using Sentinel-2 Satellite Imagery

Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

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