Detection of urban tree canopy from very high resolution imagery using an object based classification

Abstract

<span>Tree that grows within a town, city and suburban areas, collection of these trees makes the urban forest. These urban forest and urban trees have impact on urban water, pollution and heat. Nowadays we are experiencing drastic climatic changes because of cutting of trees for our growth and increasing population which leads to expansion of roads, towers, and airports. Individual tree crown detection is necessary to map the forest along with feasible planning for urban areas. In this study, using WorldView-2imagery, trees in specific area are detected with object-based image analysis (OBAI) approach. Therefore with improvement in spatial and spectral resolution of an image, extracted from WorldView-2 carried out urban features with better accuracy. The aim of this research is to illustrate how object-based method can be applied to the available data to accurately find out vegetation, which can be further sub-classified to obtain area under tree canopy. The result thus obtained gives area under tree canopy with an accuracy of 92.43 % and a Kappa coefficient of 0.80.</span>

An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

“…In this research considered five UIEB images and all these images are enhanced with our proposed method and then compared their performances with various existing methods [20]- [24] using above mentioned performance metrics. Table 1 describes the average values of the proposed and existing algorithms for various performance metrics like UIQM, UCIQE, PCQI, average gradient, edge intensity and entropy for UIEB images of proposed and existing algorithm [25], [26]. From this table, it is observed that, proposed method being produce better performance in all metric except PCQI values.…”