6 results about "Spectral variation" patented technology

The application provides a fault identification method and system based on multi-element spectral mineral anomaly analysis, relates to the fields of geology, spectroscopy, petrology and engineering geology, and a specific scheme comprises the following steps: in-situ testing of tunnel surrounding rock is carried out by using a visible light-near infrared, mid-infrared and laser Raman spectrometer, and mixed spectral variation characteristics are extracted; based on the mixed spectral variation characteristics, the mineral composition of the tunnel surrounding rock is identified according to the spectral identification standard of the abnormal mineral combination of the fault zone, quantitative inversion of the minerals is carried out, and the mineral content is obtained; according to the mineral anomaly mode of the fault zone, mineral anomaly analysis is carried out on the mineral composition, and the mineral anomaly type is obtained; based on the differential change of the mineral content and the mineral anomaly type, mineralogical anomaly characteristic analysis is carried out, and the fault identification result is output; the application replaces the experience knowledge of traditional geologists with quantitative information of multi-element spectral mineral anomaly, and further improves the accuracy and efficiency of fault identification in tunnels and underground engineering.

This invention discloses a method for establishing a spectral characteristic database of trees affected by wood nematode, involving the interdisciplinary fields of forestry disease monitoring, spectral analysis, and database technology. By capturing subtle spectral changes in pine needles in the red and near-infrared bands, this invention can identify signs of infection before obvious symptoms appear on the tree's surface. Through a multi-step, multi-index comprehensive analysis method, it effectively eliminates interference from other ground features such as forest land and bare soil. The established dedicated spectral model and indices are highly targeted, achieving higher classification and identification accuracy than using a single general index, significantly reducing the false positive rate. The use of UAV hyperspectral remote sensing technology overcomes the shortcomings of manual inspection, such as limited coverage, numerous blind spots, and poor timeliness, enabling rapid and comprehensive monitoring of large-scale pine forest resources. It can not only determine whether pine trees are infected but also quantitatively assess the severity of the disease through chlorophyll content estimation models and spectral characteristic differences.

The application discloses a kind of position-aware subgraph convolution network-based hyperspectral image classification methods, comprising: data preprocessing is carried out to hyperspectral image data;Utilize subgraph convolution neural network and fuse pixel position coding to establish hyperspectral image classification model;Based on the hyperspectral image data after pre-processing, the hyperspectral image classification model is trained, and the hyperspectral image classification model is obtained;The overall hyperspectral image pixel class is classified using the hyperspectral image classification model.The application proposes a subgraph convolution neural network method that efficiently fuses laplace-based sign-invariant position coding, which can enhance pixel feature diversity, reduce classification bias caused by spectral variation, and significantly improve classification accuracy.

This invention discloses a Hapke hyperspectral autoencoder unmixing method for spectral variations. Addressing the problem of spectral variations in hyperspectral images, this method combines the Hapke physical model of spectral imaging with a deep network to construct a unmixing network architecture for the Hapke model. This effectively reduces the impact of spectral variations on endmember extraction, resulting in more accurate endmembers and abundances. The core of this method is to construct a deep network to learn the spectral variation parameters in the Hapke model. Based on the autoencoder unmixing network architecture, this invention extracts abundance features through convolutional autoencoder layers, while the weights of the decoding layer are endmember features. To reduce the impact of spectral variations on endmembers and abundances, the Hapke physical model is fused with the network, and a parameter estimation network is designed to learn the spectral variation parameters, which are then used as illumination-topography attention applied to abundances. Simultaneously, these parameters are substituted into the Hapke model, transforming endmember features into single-scattering albedo. Combined with reflectance and albedo spatial losses, iterative optimization of endmember abundances is achieved. Numerical experiments show that the proposed unmixing method has good unmixing performance and significantly improves the accuracy of endmember estimation.

This application relates to the fields of remote sensing image processing and environmental monitoring technology, and discloses a remote sensing monitoring method for soil salinization in semi-arid regions based on small samples. The method extracts regional baseline endmembers and performs fully constrained linear spectral unmixing to obtain the baseline reconstruction error. This error is used to screen high-confidence unlabeled pixels to generate pseudo-labels, constructing an enhanced training sample set. An ensemble regression model capable of outputting predicted mean and variance is trained. For pixels with high predicted variance, an endmember elastic perturbation mechanism is triggered, iteratively optimizing under the condition of satisfying physical reconstruction error constraints to obtain the endmember combination and inversion results that minimize the predicted variance. Finally, the results are integrated to generate a salinity distribution map. This invention solves the problem of small-sample training through a physical-statistical coupling strategy and uses adaptive perturbation to correct spectral variations in mixed pixels, effectively improving the inversion accuracy and reliability of results in complex surface environments.