Research On MRF Segmentation Method For Forest Canopy Hemisphere Image

Leaf area index(LAI)is an important structural parameter of forest canopy,which is associated with many biological and physical processes in forest ecosystems.Digital hemispherical photography(DHP)is one of the most widely used methods for measuring LAI because of its simplicity,speed,versatility and non-destructiveness.However,there are still some technical problems in this method.One of the key points is the segmentation of the forest canopy image.Since image segmentation is the first step in image analysis of DHP,the accuracy of segmentation will seriously affect subsequent data processing,as well as the results of LAI calculations.In view of this,this thesis has carried out related research on the segmentation method of forest canopy hemisphere image.The main research contents are as follows:Aiming at the influence of non-imaging pixels on segmentation in canopy hemisphere images,an imaging region extraction algorithm based on circular Hough transform(CHT)is proposed.First,the minimum error method is used to segment the canopy image.Then,the mathematical morphology method is used to obtain the boundary points.Finally,the circular parameters of the imaging region are obtained by processing the boundary points with CHT,thereby extracting the effective imaging area of the forest canopy hemisphere image.This method makes it unnecessary to consider non-imaging pixels for subsequent processing,which facilitates image segmentation.Aiming at the complexity of forest canopy hemisphere images,Markov random field(MRF)image segmentation method was used to segment it.The method is compared with traditional algorithms(Otsu method,3D Otsu method,maximum entropy method,K-means clustering algorithm,fuzzy C-means clustering algorithm).Experiments show that the method has a good segmentation effect.In order to further improve the segmentation accuracy of MRF image segmentation method applied to canopy hemisphere images,an improved MRF segmentation method for forest canopy hemisphere image is proposed.Firstly,a probability distribution model similar to the real distribution is constructed and applied to the observation field,so that the observation field model is more in line with the real information of the image to obtain more effective segmentation results.Then through the proposed global optimization algorithm,a more optimized segmentation result is obtained.Finally,a de-reflection post-processing algorithm is used to identify and correct the reflective pixels,thereby suppressing the influence of the reflection phenomenon on the image segmentation.The experimental results show that the above improvements improve the segmentation accuracy of the algorithm.Taking the image segmentation method proposed in this paper as the core,a forest canopy image acquisition and processing system is built to complete the specific implementation of the segmentation method.The system collects the forest canopy image through the 180° fisheye camera,and realizes the image segmentation through the Raspberry Pi development platform with the OpenCV library installed,and uses the 7-inch touch screen for human-computer interaction and display of processing results.The system has the advantages of low cost,small size,easy movement,high integration,convenient operation,etc.,and has certain practical application value.