- 标题
- 摘要
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- 实验方案
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Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data
摘要： The global availability of high spatial resolution images makes mapping tree species distribution possible for better management of forest resources. Previous research mainly focused on mapping single tree species, but information about the spatial distribution of all kinds of trees, especially plantations, is often required. This research aims to identify suitable variables and algorithms for classifying land cover, forest, and tree species. Bi-temporal ZiYuan-3 multispectral and stereo images were used. Spectral responses and textures from multispectral imagery, canopy height features from bi-temporal stereo imagery, and slope and elevation from the stereo-derived digital surface model data were examined through comparative analysis of six classification algorithms including maximum likelihood classifier (MLC), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The results showed that use of multiple source data—spectral bands, vegetation indices, textures, and topographic factors—considerably improved land-cover and forest classification accuracies compared to spectral bands alone, which the highest overall accuracy of 84.5% for land cover classes was from the SVM, and, of 89.2% for forest classes, was from the MLC. The combination of leaf-on and leaf-off seasonal images further improved classification accuracies by 7.8% to 15.0% for land cover classes and by 6.0% to 11.8% for forest classes compared to single season spectral image. The combination of multiple source data also improved land cover classification by 3.7% to 15.5% and forest classification by 1.0% to 12.7% compared to the spectral image alone. MLC provided better land-cover and forest classification accuracies than machine learning algorithms when spectral data alone were used. However, some machine learning approaches such as RF and SVM provided better performance than MLC when multiple data sources were used. Further addition of canopy height features into multiple source data had no or limited effects in improving land-cover or forest classification, but improved classification accuracies of some tree species such as birch and Mongolia scotch pine. Considering tree species classification, Chinese pine, Mongolia scotch pine, red pine, aspen and elm, and other broadleaf trees as having classification accuracies of over 92%, and larch and birch have relatively low accuracies of 87.3% and 84.5%. However, these high classification accuracies are from different data sources and classification algorithms, and no one classification algorithm provided the best accuracy for all tree species classes. This research implies the same data source and the classification algorithm cannot provide the best classification results for different land cover classes. It is necessary to develop a comprehensive classification procedure using an expert-based approach or hierarchical-based classification approach that can employ specific data variables and algorithm for each tree species class.
关键词： classification,ZiYuan-3,stereo image,machine learning,tree species
更新于2025-09-19 17:15:36
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[IEEE 2018 International Russian Automation Conference (RusAutoCon) - Sochi, Russia (2018.9.9-2018.9.16)] 2018 International Russian Automation Conference (RusAutoCon) - System of Stereovision Based on Fuzzy-Logical Method of Constructing Depth Map
摘要： This article considers the distance measurement to objects using video processing to construct a depth map using the modified algorithm of sum of absolute differences (SAD). According to the measured distance to the objects, the required rotation angle of the mobile robot is given to travel around the obstacle. The SAD algorithm is implemented using a fuzzy-logical system with soft arithmetic operations in the structure of fuzzy inference. In the experimental part, a depth map is constructed to measure the distance to the object.
关键词： depth map,stereo image,fuzzy logic,disparity map,sum of absolute differences (SAD)
更新于2025-09-10 09:29:36
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Implementing a Stereo Image Correction Method for 3D Surgery Microscopes
摘要： Recently 3D scenes are used in various industrial fields such as medical applications, computer games, surface examinations, biology and others. 3D optical microscopes can show extremely precise details in 3D. To reconstruct the 3D images of an optical microscope, two cameras are mounted on the optical microscope. Incoming images through an object lens of an optical microscope are projected on sensors of mounted cameras by using refraction mirrors. Two cameras capture the left and right images to reconstruct the final 3D images. In this paper, we correct the 3D reconstruction errors with the SURF algorithm. We also design a hardware system to correct wrong mirror positions using servo motors. In addition, we propose the surgical system with HMD and wireless communications. This system would be helpful for doctors due to make doctors comfortable and it can be used to educate surgery procedures.
关键词： Stereo Image,Optical Microscope,SURF,Medical Device,Computer Vision
更新于2025-09-09 09:28:46