光谱成像技术研究室知识库

Aiming at the problem of segmentation parameters setting in object-oriented hyperspectral classification method,an adaptive hyperspectral classification algorithm based on region-growing techniques was proposed in this paper.Firstly,a constrained region-growing method was proposed,which used the spatial information of the training samples to provide effective constraints,thus reducing the error propagation rate of the region markers in the region-growing process,and improving classification performance.Secondly,an adaptive threshold calculation method was proposed.By analyzing the distribution law of the spectrum of the training samples,the reasonable threshold for region division was calculated adaptively to replace the empirical threshold,so that the robustness of the algorithm was improved.Finally,the K-nearest neighbor algorithm (KNN)was used to classify the centers of each region after division.Experimental results show that:For different images,the adaptive thresholds calculated by the method are consistent with the empirical values,and the classification effect of the proposed algorithm is better than other algorithms.For hyperspectral data Indian Pines from AVIRIS sensor,the overall classification accuracy and kappa are 92.94% and 0.919 5 respectively with 10%training samples,and for hyperspectral data Pavia University from ROSIS sensor,the overall classification accuracy and kappa are 95.78%and 0.944 0 respectively with 5%training samples. The proposed algorithm not only enhances the robustness of the algorithm,but also improves the classification performance effectively,and has strong practicability in hyperspectral applications.