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Abstract:The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI) series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%-10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest classification by fusing multi-sensor data can retrieve better wetland landcover information than the other classifiers, which is significant for the monitoring and management of the wetland ecological resources in arid areas.

In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset.

N2,N2,N8,N8‐tetrakis(4‐(methylthio)phenyl)dibenzo[b,d]thiophene‐2,8‐diamine (DBTMT) is synthesized from three commercial monomers for application as a promising dopant‐free hole‐transport material (HTM) in perovskite solar cells (pero‐SCs). The intrinsic properties (optical properties and electronic energy levels) of DBTMT are investigated, proving that DBTMT is a suitable HTM for the planar p–i–n pero‐SCs. The champion power conversion efficiency (PCE) of the optimized pero‐SCs (with structure as ITO/pristine DBTMT/MAPbI3/C60/BCP/Ag) reaches 21.12% with a fill factor (FF) of 83.25%, which is among the highest PCEs and FFs reported for planar p–i–n pero‐SCs based on dopant‐free HTMs. The Fourier‐transform infrared spectroscopy, X‐ray diffraction, and X‐ray photoelectron spectroscopy spectra of MAPbI3 and DBTMT–MAPbI3 films demonstrate that there is an interaction between DBTMT and MAPbI3 at the interface through the sulfur atoms in DBTMT to passivate the defects, which is corresponding to the higher FF and PCE of the corresponding device.

For commercial applications, it is a challenge to find suitable and low-cost hole-transporting material (HTM) in perovskite solar cells (PSCs), where high efficiency spiro-OMeTAD and PTAA are expensive. A HTM based on 9,9-dihexyl-9H-fluorene and N,N-di-p-methylthiophenylamine (denoted as FMT) is designed and synthesized. High-yield FMT with a linear structure is synthesized in two steps. The dopant-free FMT-based planar p-i-n perovskite solar cells (pp-PSCs) exhibit a high power conversion efficiency (PCE) of 19.06%, which is among the highest PCEs reported for the pp-PSCs based on organic HTM. For comparison, a PEDOT:PSS HTM-based pp-PSC is fabricated under the same conditions, and its PCE is found to be 13.9%.