This study compared two object-oriented land use change detection methods-detection after classification (DAC) and classification after detection (CAD) -based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.Environmental monitoring and natural resources management require the considerably urgent development of operational solutions that can extract tangible information from remote sensing data [1]. Satellite remote sensing has been applied in the mapping of land use since the 1970s, and nearly all types of sensors have been studied [2,[3][4][5]. Different types of sensors have different advantages;Landsat images are extensively used in obtaining free and large swath, especially in large scale [6][7][8][9].However, while the need for information continues to grow, only a small fraction of stored information in satellite data is often tapped. Transitioning magnanimity data into robust information has never been so important.Change detection, which is the process of identifying change in an area of an object or a phenomenon by observing it at different times, is an important way of obtaining information using the remote sensing technology [10]. The process uses multitemporal datasets to qualitatively analyze the temporal effects of objects or phenomena and quantify changes [11]. Land use/land cover change Preprints (www.preprints.org) | NOT PEER-REVIEWED |

Objective: The burden of cognitive impairment in end-stage renal disease (ESRD) undergoing peritoneal dialysis received more attention, which associated with hospitalization, mortality and quality of life. We aimed to assess the topological alterations of brain white matter structural network in ESRD and the correlation between network metrics with Montreal Cognitive Assessment scores and clinical data. Methods: The study included 25 ESRD patients with secondary hyperparathyroidism (SHPT group), 25 patients without SHPT (Non-SHPT group) and 25 healthy controls (HC group) of comparable age and sex. Cognitive function was assessed using Montreal Cognitive Assessment. WM structural network constructed by diffusion tensor imaging and deterministic tractography method, and then used graph theoretical approaches to investigate alterations in the global and regional properties of the WM network in these participants. Results: ESRD patients showed cognitive impairment compared to HC and SHPT patients was lower cognitive scores than Non-SHPT patients. The global topological organization and local efficiency of WM network was significantly disrupted in SHPT but not in Non-SHPT patients, as compared with HC group. Moreover, lower regional efficiency was found in ESRD patients mainly distributed in the frontal and parietal cortices. In addition, association was found between iPTH, shortest path length and cognitive impairment, and iPTH level was negatively correlated with small-worldness by two indexes normalized clustering coefficient and normalized shortest path length. Conclusion: The present study indicated that brain structural connectome in ESRD patients with high iPTH level was disrupted as cognitive impairment and it has the potential connectome-based biomarkers for early detection.