Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

Yang, Shuting; Gu, Lingjia; Li, Xiaofeng; Jiang, Tao; Ren, Ruizhi

doi:10.3390/rs12193119

Cited by 68 publications

(36 citation statements)

References 51 publications

(31 reference statements)

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“…The vegetation index can mitigate the effects of the atmosphere, terrain, and thin clouds [ 52 , 53 ]. Some studies also demonstrated that spatial features such as texture information could effectively improve extraction accuracy when using mono-temporal images [ 54 , 55 ]. However, because this study focused on which band and which bands combination were more effective for garlic extraction, no additional features were used.…”

Section: Discussionmentioning

confidence: 99%

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

Han

Guan

et al. 2021

Sensors

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Garlic is one of the main economic crops in China. Accurate and timely extraction of the garlic planting area is critical for adjusting the agricultural planting structure and implementing rural policy actions. Crop extraction methods based on remote sensing usually use spectral–temporal features. Still, for garlic extraction, most methods simply combine all multi-temporal images. There has been a lack of research on each band’s function in each multi-temporal image and optimal bands combination. To systematically explore the potential of the multi-temporal method for garlic extraction, we obtained a series of Sentinel-2 images in the whole garlic growth cycle. The importance of each band in all these images was ranked by the random forest (RF) method. According to the importance score of each band, eight different multi-temporal combination schemes were designed. The RF classifier was employed to extract garlic planting area, and the accuracy of the eight schemes was compared. The results show that (1) the Scheme VI (the top 39 bands in importance score) achieved the best accuracy of 98.65%, which is 6% higher than the optimal mono-temporal (February, wintering period) result, and (2) the red-edge band and the shortwave-infrared band played an essential role in accurate garlic extraction. This study gives inspiration in selecting the remotely sensed data source, the band, and phenology for accurately extracting garlic planting area, which could be transferred to other sites with larger areas and similar agriculture structures.

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Section: Discussionmentioning

confidence: 99%

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

Han

Guan

et al. 2021

Sensors

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“…The performance of classification with limited inputs can be improved by applying a hybrid model with multiple classifiers where each individual classifier exhibits complementary behavior [34][35][36]. The combination of the DL model with the ML model can achieve better classification accuracy than each single model.…”

Section: Introductionmentioning

confidence: 99%

“…Such a hybrid model combining a CNN as a feature extractor with RF as a sophisticated classifier (herein referred to as CNN-RF) has been proposed for the supervised classification. Moreover, its effectiveness when insufficient information is used for the learning process is demonstrated, compared with conventional CNN that is susceptible to overfitting problems due to input information deficiency [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…Instead of applying advanced models, most previous studies on early crop mapping have focused on either the construction of a sufficient time-series SAR image set [11], or the fusion of optical and SAR images [39]. Yang et al [35] applied CNN-RF for crop classification with multi-temporal Sentinel-2 images but focused on the selection of optimal features. Furthermore, the lack of sufficient training data has not been investigated in conjunction with different time-series lengths.…”

Section: Introductionmentioning

confidence: 99%

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Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data

et al. 2021

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When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combines the automatic feature extraction capability of CNN with the superior discrimination capability of an RF classifier. Two experiments on incremental crop classification with unmanned aerial vehicle images were conducted to compare the performance of CNN-RF with that of CNN and RF with respect to the length of the time-series and training data sizes. When sufficient time-series images and training data were used for the classification, the accuracy of CNN-RF was slightly higher or comparable with that of CNN. In contrast, when fewer images and the smallest training data were used at the early crop growth stage, CNN-RF was substantially beneficial and the overall accuracy increased by maximum 6.7%p and 4.6%p in the two study areas, respectively, compared to CNN. This is attributed to its ability to discriminate crops from features with insufficient information using a more sophisticated classifier. The experimental results demonstrate that CNN-RF is an effective classifier for early crop mapping when only limited input images and training samples are available.

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“…For different classification targets, the accuracy will be different by using spectral features, texture features, or a combination of multiple features at different times [30]. More and more researchers combine multiple features [20,31,32] of multi-temporal images [33][34][35][36] to improve classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning

Yang

Zhang

et al. 2021

Remote Sensing

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The jujube industry plays a very important role in the agricultural industrial structure of Xinjiang, China. In recent years, the abandonment of jujube fields has gradually emerged. It is critical to inventory the abandoned land soon after it is generated to adjust agricultural production better and prevent the negative impacts from the abandonment (such as outbreaks of diseases, insect pests, and fires). High-resolution multi-temporal satellite remote sensing images can be used to identify subtle differences among crops and provide a good tool for solving this problem. In this research, both field-based and pixel-based classification approaches using field boundaries were used to estimate the percentage of abandoned jujube fields with multi-temporal high spatial resolution satellite images (Gaofen-1 and Gaofen-6) and the Random Forest algorithm. The results showed that both approaches produced good classification results and similar distributions of abandoned fields. The overall accuracy was 91.1% for the field-based classification and 90.0% for the pixel-based classification, and the Kappa was 0.866 and 0.848 for the respective classifications. The areas of abandoned land detected in the field-based and pixel-based classification maps were 806.09 ha and 828.21 ha, respectively, accounting for 8.97% and 9.11% of the study area. In addition, feature importance evaluations of the two approaches showed that the overall importance of texture features was higher than that of vegetation indices and that 31 October and 10 November were important dates for abandoned land detection. The methodology proposed in this study will be useful for identifying abandoned jujube fields and have the potential for large-scale application.

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Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

Cited by 68 publications

References 51 publications

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data

Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning

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