Classification of Land Cover in Complex Terrain Using Gaofen-3 SAR Ascending and Descending Orbit Data

Wang, Hongxia; Yang, Huazhong; Huang, Yabo; Wu, Lin; Guo, Zhengwei; Li, Ning

doi:10.3390/rs15082177

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…In order to measure the accuracy of the classification results, qualitative and quantitative evaluations were made. Qualitative evaluation was applied to evaluate the classification results intuitively [51]. Compared with the samples obtained from ground-truth data, the classification results were interpreted and verified intuitively.…”

Section: Ground-truth Data and Preprocessingmentioning

confidence: 99%

Synergy of Sentinel-1 and Sentinel-2 Imagery for Crop Classification Based on DC-CNN

et al. 2023

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Over the years, remote sensing technology has become an important means to obtain accurate agricultural production information, such as crop type distribution, due to its advantages of large coverage and a short observation period. Nowadays, the cooperative use of multi-source remote sensing imagery has become a new development trend in the field of crop classification. In this paper, the polarimetric components of Sentinel-1 (S-1) decomposed by a new model-based decomposition method adapted to dual-polarized SAR data were introduced into crop classification for the first time. Furthermore, a Dual-Channel Convolutional Neural Network (DC-CNN) with feature extraction, feature fusion, and encoder-decoder modules for crop classification based on S-1 and Sentinel-2 (S-2) was constructed. The two branches can learn from each other by sharing parameters so as to effectively integrate the features extracted from multi-source data and obtain a high-precision crop classification map. In the proposed method, firstly, the backscattering components (VV, VH) and polarimetric components (volume scattering, remaining scattering) were obtained from S-1, and the multispectral feature was extracted from S-2. Four candidate combinations of multi-source features were formed with the above features. Following that, the optimal one was found on a trial. Next, the characteristics of optimal combinations were input into the corresponding network branches. In the feature extraction module, the features with strong collaboration ability in multi-source data were learned by parameter sharing, and they were deeply fused in the feature fusion module and encoder-decoder module to obtain more accurate classification results. The experimental results showed that the polarimetric components, which increased the difference between crop categories and reduced the misclassification rate, played an important role in crop classification. Among the four candidate feature combinations, the combination of S-1 and S-2 features had a higher classification accuracy than using a single data source, and the classification accuracy was the highest when two polarimetric components were utilized simultaneously. On the basis of the optimal combination of features, the effectiveness of the proposed method was verified. The classification accuracy of DC-CNN reached 98.40%, with Kappa scoring 0.98 and Macro-F1 scoring 0.98, compared to 2D-CNN (OA reached 94.87%, Kappa scored 0.92, and Macro-F1 scored 0.95), FCN (OA reached 96.27%, Kappa scored 0.94, and Macro-F1 scored 0.96), and SegNet (OA reached 96.90%, Kappa scored 0.95, and Macro-F1 scored 0.97). The results of this study demonstrated that the proposed method had significant potential for crop classification.

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Section: Ground-truth Data and Preprocessingmentioning

confidence: 99%

Synergy of Sentinel-1 and Sentinel-2 Imagery for Crop Classification Based on DC-CNN

et al. 2023

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“…In the event of disasters and wars, PolSAR has timely and effective ground-mapping capabilities [3,4]. Using polarimetry, four polarizations (HH, VV, HV, and VH) are used to illuminate the ground targets, making them sensitive to the shape, orientation, and dielectric properties of illuminated targets and capable of distinguishing different targets [5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

PolSAR Image Classification by Introducing POA and HA Variances

Lan,

Liu,

et al. 2023

Remote Sensing

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A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target classification. However, current methods experience difficulties in separating forests and buildings, especially oriented buildings. To address this issue, inspired by the three-component decomposition method, multiple new scattering models were proposed to describe the difference between forest scattering and building scattering. However, this problem cannot effectively be solved with scattering power alone since HV polarization records significant scattering powers from building areas that are similar to vegetation. Therefore, in this study, two new parameters, the polarization orientation angle (POA) variance and helix angle (HA) variance, were defined to describe the distributions of buildings and forests. By combining scattering power with POA and HA variances, the random forest algorithm was used to conduct the land cover classification, focusing on distinguishing between forests and oriented buildings. Finally, the C- and L-band polarimetric SAR data acquired by the GF-3, ALOS1 PALSAR, and SAOCOM systems were selected to test the proposed method. The results indicate that it is feasible to improve PolSAR classification accuracy by introducing polarimetric parameters. Quantitatively, the classification accuracies increased by 23.78%, 10.80%, and 12.97% for the ALOS1 PALSAR, GF-3, and SAOCOM data, respectively.

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Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China)

Liang,

Dai,

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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Classification of Land Cover in Complex Terrain Using Gaofen-3 SAR Ascending and Descending Orbit Data

Cited by 5 publications

References 32 publications

Synergy of Sentinel-1 and Sentinel-2 Imagery for Crop Classification Based on DC-CNN

Synergy of Sentinel-1 and Sentinel-2 Imagery for Crop Classification Based on DC-CNN

PolSAR Image Classification by Introducing POA and HA Variances

Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China)

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