įor successful applications of microwave remote sensing endeavors it is essential to understand how surface targets respond to changing synthetic aperture radar (SAR) parameters. Feature values range from zero to a maximum (4.3 × 10 9 for 32- bit data) that is dependent on the bit depth of the. Each polarization layer mean value (c L ) is calculated from the polarization values (c Li ) of all n pixels forming an image object. The feature used for classification is the mean SAR value within each object. Each object contained a minimum of 650 pixels, with larger objects containing as many as 9000 pixels per object. Once the objects were generated, radar averages for each polarization were extracted for each polygon. Four objects were appropriate when delineating yield in the wheat field. The higher pedodiversity in the corn field resulted in a larger number of objects available for training (10 objects) and for validation (15 objects). Because of the low pedodiversity in the wheat field, there were a limited number of polygons available for classification training (four objects) and validation (six objects). This resulted in 10 objects for classification of the wheat field and 25 objects for classification of the corn field. eCognition was used to generate similar polygons to perform object-based classification using the SAR data ( Figure 2b). These zones have multiple polygons but only two classes ( Figure 2a). For example, there may be two or three homogenous zones that can be statistically separated using the EC data.
Ecognition 9.2 Crack software#
the objects or polygons are generated based on homogenous zones in eCognition, the software generates a greater number of objects compared with the number of polygons discriminated using statistical separability (Figure 1).
Due to complexity and heterogeneity of floodplain environments, however, it is difficult to map wetlands accurately over a large area as the CARB. To address this issue, we developed a novel and robust classification approach integrating image compositing algorithm, objected-based image analysis, and hierarchical random forest classification, named COHRF, to delineate floodplain wetlands and surrounding land covers. Based on the COHRF classification approach, 4622 Landsat images were applied to produce a 30-m resolution dataset characterizing dynamics and conversions of floodplain wetlands in the CARB during 1990–2018. Results show that (1) all floodplain land cover maps in 1990, 2000, 2010, and 2018 had high mapping accuracies (ranging from 90 %☐.001–97%☐.005), suggesting that COHRF is a robust classification approach (2) CARB experienced an approximately 25 % net loss of floodplain wetlands with an area declined from 8867 km 2 to 6630 km 2 during 1990–2018 (3) the lost floodplain wetlands were mostly converted into croplands, while, there were 111 km 2 and 256 km 2 of wetlands rehabilitated from croplands during periods of 2000–20–2018, respectively. To our knowledge, this study is the first attempt that focus on delineating floodplain wetlands at a large-scale and produce the first 30-m spatial resolution dataset demonstrating long-term dynamics of floodplain wetlands in the CARB. The COHRF classification approach could be used to classify other ecosystems readily and robustly. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. The work described in this article builds on so-called rule sets. SPEECH RECOGNITION EDITING.A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects’ properties in these varying images is hardly predictable. It focuses on object-based analysis and data fusion and integrated analyses. ECognition is a suite of software tools and algorithms for image analysis and classification developed by Trimble Geospatial Inc.
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