Normalized Difference Vegetation Index （NDVI） is a very useful feature for differentiating vegetation and non-vegetation in remote sensed imagery. In the light of the function of NDVI and the spatial patterns of the...Normalized Difference Vegetation Index （NDVI） is a very useful feature for differentiating vegetation and non-vegetation in remote sensed imagery. In the light of the function of NDVI and the spatial patterns of the vegetation landscapes, we proposed the lacunarity texture derived from NDVI to characterize the spatial patterns of vegetation landscapes concerning the ＂gappiness＂ or ＂emptiness＂ characteristics. The NDVI-based lacunarity texture was incorporated into object-oriented classification for improving the identification of vegetation categories, especially Torreya which was the targeted tree species in the present research. A three-level hierarchical network of image objects was defined and the proposed texture was integrated as potential sources of information in the rules base. A knowledge base of rules created by classifier C5.0 indicated that the texture could potentially be applied in object-oriented classification. It was found that the addition of such texture improved the identification of every vegetation category. The results demonstrated that the texture could characterize the spatial patterns of vegetation structures, which could be a promising approach for vegetation identification.展开更多
You Complying with the school＇s education purpose of training applied talents, in colleges and universities, teaching-oriented teaching team should be read in conjunction with the construction of its evaluating index...You Complying with the school＇s education purpose of training applied talents, in colleges and universities, teaching-oriented teaching team should be read in conjunction with the construction of its evaluating index system as well as the improvement of the operation quality of teaching efficiency. In view of vague characteristic of evaluation, this paper uses analytic hierarchy process to carry on the fuzzy and synthetic appraisal for operation quality of teaching-oriented teaching teams in colleges and universities, reasonably ascertaining the weights of the evaluation indexes and improving the objectivity and accuracy of evaluation.展开更多
基金supported by the National Natural Science Foundation of China (30671212)
文摘Normalized Difference Vegetation Index （NDVI） is a very useful feature for differentiating vegetation and non-vegetation in remote sensed imagery. In the light of the function of NDVI and the spatial patterns of the vegetation landscapes, we proposed the lacunarity texture derived from NDVI to characterize the spatial patterns of vegetation landscapes concerning the ＂gappiness＂ or ＂emptiness＂ characteristics. The NDVI-based lacunarity texture was incorporated into object-oriented classification for improving the identification of vegetation categories, especially Torreya which was the targeted tree species in the present research. A three-level hierarchical network of image objects was defined and the proposed texture was integrated as potential sources of information in the rules base. A knowledge base of rules created by classifier C5.0 indicated that the texture could potentially be applied in object-oriented classification. It was found that the addition of such texture improved the identification of every vegetation category. The results demonstrated that the texture could characterize the spatial patterns of vegetation structures, which could be a promising approach for vegetation identification.
文摘You Complying with the school＇s education purpose of training applied talents, in colleges and universities, teaching-oriented teaching team should be read in conjunction with the construction of its evaluating index system as well as the improvement of the operation quality of teaching efficiency. In view of vague characteristic of evaluation, this paper uses analytic hierarchy process to carry on the fuzzy and synthetic appraisal for operation quality of teaching-oriented teaching teams in colleges and universities, reasonably ascertaining the weights of the evaluation indexes and improving the objectivity and accuracy of evaluation.
