Tubing segments, generated during red blood cell (RBC) component production, were tested to determine their suitability as a sample source for quality testing. Nondestructive testing of blood components could permit in-process quality control and reduce discards. Kurach, Jayme D R Hansen, Adele L Turner, Tracey R Jenkins, Craig Acker, Jason P Segments from red blood cell units should not be used for quality testing. However, BOD and COD values do not show a steady increase in each segment Water quality is closely related to the surrounding land use.Therefore, it can not be concluded that the water quality downstream is worse than in the upstream area. in general, BOD and COD values have increased from upstream to downstream. Land use classification showed that river segment that has more undeveloped area has better water quality compared to river segment with developed area. This study compared water quality with land use condition in each segment of river. The main objectives with this study were to examine if there is a correlation between land use and water quality in Cisadane River and there is a difference in water quality between the upstream section of Cisadane River compared with its downstream section. Cisadane River is an interesting case for investigating the effect of land use to water quality and comparing water quality in every river segment. Cisadane River is one of the river in Indonesia where urbanization, industrialization, and agricultural are extremely main sources of pollution. The growth of population and industrialization combined with land development along river cause water pollution and environmental deterioration. Water quality of Cisadane River based on watershed segmentationĮffendi, Hefni Ayu Permatasari, Prita Muslimah, Sri Mursalin Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. The method is tested on two segmentation algorithms and three testing images. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. These two indicators are then combined into a global assessment metric as the final quality score. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation.
Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). White Springs.A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images
0 kommentar(er)
