- freely available
Remote Sensing 2017, 9(11), 1184; doi:10.3390/rs9111184
2. Study Area and Datasets
2.1 Study Area
2.2. GF-1 WFV Data
2.3. In-Season Sample Data
3.1. Overview of In-Season Crop Classification
- A multiresolution algorithm was used for image segmentation and the appropriate segmentation scale and the parameters associated with heterogeneity criterion were selected according to local variance;
- Evaluation on the performance of the features for different crop types according to their types (spectral reflectance, texture, temporal features and vegetation indexes);
- Analysis of the contribution of different feature types to the classification accuracy.
3.2. Image Segmentation
3.3. Feature Extraction
- S: The spectral features from a single image per season were taken as input. Only four available spectral bands of each scene were selected.
- STx’: The spectral bands and texture features acquired from a single image were taken as input. Four available spectral bands (4 features) and GLCM correlation, GLCM dissimilarity, and GLCM entropy from each band (12 features) acquired in specific season were selected. This experiment represents the case where spatio-spectral feature type information is employed for crop identification.
- SV: In addition to spectral features, NDVI, EVI, RVI and RI from GF-1 WFV data acquired in specific season were taken as input to enhance the spectral information. This experiment represents the case where multiple spectral information but little temporal information and non-spatial information are employed for crop identification.
- SVTx’: Along with the spatio-spectral features from a single image, vegetation indices were taken as input. This experiment represents the case where multiple spectral information but little temporal information and spatial information are employed for crop identification.
- TmS: Multi-temporal available spectral features collected during the crop present growth stages were taken as input. This experiment represents the traditional “multiple-dates” approaches. It is a case of employing multiple temporal information but little spectral information (without spectral enhancement, lack of vegetation indices) and non-spatial information for crop identification.
- TmSTx: Multi-temporal spectral and multi-temporal texture features were taken as the input. For each available date, the four bands and 12 texture features were selected. This experiment represents the cases of employing multiple spectral, multiple temporal and multiple texture information for crop identification.
- TmSTx’: Multi-temporal spectral and in-season texture features were taken as the input. Only 12 texture features were extracted from the special spectral bands acquired in present season. This experiment represents the case of employing multiple temporal, multiple spectral but little texture information to enhance the present information on crop structure and planting pattern for crop identification.
- TmSV: Multi-temporal spectral features and vegetation indices were taken as input. This experiment represents the case of employing multiple temporal information and multiple spectral information for crop identification.
- TmSVTx: The available spatio-temporal spectral and vegetation indices collected during the crop present growth stages were taken as input.
- TmSVTx’: Only the specific texture features were added into the multi-temporal spectral features and vegetation indices datasets.
3.4. Random Forest Classification
4.1. The Optimal Segmentation Scale of Crop Type
4.2. Performances of Different Feature Subspaces on Crop Classification
4.3. In-Season Crop Mapping
- The map in the fourth season has the highest accuracy since it has the largest number of features and thus contains more useful information for classification. Therefore, for multiple-season crop mapping, more attention should be paid to the early seasons that may suffer from the insufficient information.
- Texture can be essential information for crop mapping when there is insufficient spectral and temporal information at the beginning of crop-growing period, whereas in-season texture helps increase the chance for mature crop classification, not only in addition to multi-temporal spectral information, but also avoiding redundancy and maximizing the classification accuracy.
- Even though we focus on the Beian City in 2014, our methods can be extended to other years for in-season crop monitoring since this robust approach possesses of the time-scale scalability. In addition, future work could address the issues on how to use multi-source finer spatial resolution data to improve the quality and timeliness of in-season crop mapping.
Conflicts of Interest
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|Survey Date||Crop Types||Samples||Training Samples||Validation Samples|
|In-Season ID||Period of Mapping||Targeted Types|
|Season ID||Classification Type||UA (%)||PA (%)|
|Overall accuracy = 87.73%|
Kappa coefficient = 0.7421
|Season ID||Classification Type||UA (%)||PA (%)|
|Overall accuracy = 91.26%|
Kappa coefficient = 0.8263
|Season ID||Classification Type||UA (%)||PA (%)|
|Overall accuracy = 87.88%|
Kappa coefficient = 0.8305
|Season ID||Classification Type||UA (%)||PA (%)|
|Overall accuracy = 91.72%|
Kappa coefficient = 0.8839
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