16 results about "Spectral variation" patented technology

The invention discloses a poplar degenerated forest stand accurate restoration method, and relates to the technical field of poplar degenerated forest stand restoration, and the method comprises the steps: obtaining a multi-temporal satellite remote sensing image and an unmanned plane multi-spectral image of a target area; in combination with soil moisture content, rhizosphere respiration rate and micrometeorological data collected by ground Internet of Things sensing nodes arranged in a forest stand, geographic coordinate registration and time sequence alignment are performed on the data to form a multi-source observation data set; the method comprises the following steps: extracting vegetation indexes, short-wave infrared reflectivity, soil moisture and rhizosphere physiological parameters based on a multi-source observation data set, constructing a degradation diagnosis index fusing spectral change, moisture stress and root activity, calculating the degradation degree of each pixel, and generating a degradation level spatial distribution diagram; the area with the degradation degree reaching a set threshold value is determined as a to-be-repaired area; and selecting representative poplar individuals in the to-be-repaired area, measuring the photosynthetic ability of the individuals, and calculating the individual competition intensity in combination with the three-dimensional point cloud data.

The invention discloses a position-aware hyperspectral image classification method based on a subgraph convolutional network. The method comprises the following steps: carrying out data preprocessing on hyperspectral image data; establishing a hyperspectral image classification model by using the subgraph convolutional neural network and fusing pixel position codes; training a hyperspectral image classification model based on the preprocessed hyperspectral image data to obtain the hyperspectral image classification model; and classifying the pixel categories of the whole hyperspectral image by using the hyperspectral image classification model. The invention provides a sub-graph convolutional neural network method for efficiently fusing Laplacian-based symbol-invariant position coding, so that the diversity of pixel features can be enhanced, the classification deviation caused by spectral variation is reduced, and the classification precision is remarkably improved.

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 invention provides a hyperspectral and multispectral image fusion method and system. The method comprises the following steps: acquiring hyperspectral and multispectral data of the same spatial region; performing up-sampling on the low-resolution hyperspectral image by adopting bilinear interpolation; one-dimensional wavelet transform is utilized to extract reflection peak and spectrum change characteristics in a spectrum dimension, two-dimensional wavelet transform is utilized to extract texture and edge information in a space dimension, and pixel-by-pixel wavelet basis adaptive selection is realized through Softmax gating; multi-granularity feature modeling is realized for the spectral branches by adopting grouped multi-scale convolution, and a self-adaptive weighting mechanism is formed for the spatial branches through global average pooling, a convolution layer and Sigmoid activation so as to enhance spatial details and compensate spatial feature loss by utilizing MSI; a cross-scale two-way attention mechanism is introduced, and long-range dependence modeling and information interaction of spectrum and spatial characteristics are achieved; and a high-resolution hyperspectral image is generated through a feature aggregation and reconstruction module.

The invention relates to a hyperspectral calculation imaging technology, and aims to solve the problems that in an actual remote sensing observation environment, a single imaging mode is limited by a physical sensor, high space resolution and hyperspectral resolution are difficult to obtain at the same time, and geometry and spectrum complementarity exists among multi-source images. The invention provides a hyperspectral fusion imaging method based on topology perception and a differential homeomorphic mechanism. According to the method, differential homeomorphic transformation is introduced to construct spatial degradation priori, a dual learning framework is adopted to jointly optimize high-resolution generation and bidirectional reconstruction tasks, meanwhile, a topology sensing Transform module is utilized to explicitly model a spectrum-space double-domain manifold structure, complementary correlation among multi-source data is fully mined, and the spectrum-space double-domain manifold structure is constructed. The spatial structure consistency and the spectral fidelity of the fused image are effectively improved, the problem of fusion quality reduction caused by geometric deformation and spectral variation in a complex ground feature scene is significantly improved, and a more reliable data support is provided for applications such as remote sensing monitoring and environmental perception.

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.

The invention discloses a planetary surface typical target high-precision positioning identification system and method, and the method comprises the steps: a data enhancement module receives hyperspectral data, carries out the data enhancement of the hyperspectral data, and builds a contrast learning frame based on an enhanced sample; extracting a background sample and a prior sample in the hyperspectral data, enhancing the sample and extracting a space-spectrum combined feature, combining the space-spectrum combined feature with a contrast learning framework to calculate a contrast loss, and performing sample reconstruction on the background sample; the weight reconstruction module calculates a reconstruction error of an original hyperspectrum and a prior sample, calculates a deviation vector according to the reconstruction error, converts the deviation vector into a reconstruction weight, and dynamically adjusts the comparison loss by using the reconstruction weight; the system trained by the modules performs feature extraction and calculates feature similarity to generate a target detection image; according to the method, high-precision and robust typical target positioning and recognition can be realized in a planetary surface environment which is lack of labeled samples and has spectral change.

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.

The invention relates to the technical field of spectral detection, in particular to a multi-spectral detection method for bacteria in the early stage of surgical wound infection, which comprises the following steps: scanning a target wound at a preset time interval by using a multi-spectral imaging technology to obtain spectral data, and then acquiring a multi-spectral detection result based on continuously monitored data; the spectrum signal availability is analyzed, and the comprehensive possibility that the pixels of the image target area belong to the early bacterial infection state is obtained by combining the characteristics of wound secretion and the spectrum change trend caused by the change of the bacterial chemical component proportion along with time, so that the early identification and evaluation of the infection state are realized, the false positive and false negative are reduced, and the detection accuracy is improved. The sensitivity and the specificity of early bacteria detection are remarkably improved, and accurate support is provided for clinical early intervention and debridement.

The application belongs to the technical field of optical measurement, and discloses an implementation device and method of a single-photon spectrometer, which comprises a spectral wavelength scanning module, collects a wide-spectrum light signal, carries out wave separation processing, and calculates a photon counting rate between adjacent wavelengths; wavelength steps are dynamically adjusted according to the photon counting rate, adaptive scanning of spectral changes is realized, and a single-spectrum light signal is output; a self-focusing detection module converts the single-spectrum light signal into a photon event through a preset array channel; in a preset time window, the variance and Shannon entropy of each photon event are calculated, and a local uncertainty index is constructed; spectral domain self-focusing and resampling are carried out according to the local uncertainty index, and photon counting data are obtained; high-precision, high-stability and high-efficiency spectral light signal detection and visual analysis are realized.

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.

The application belongs to the technical field of solar cells, and relates to a perovskite gradient band gap film and a preparation method and application thereof. The perovskite gradient band gap film with different band gap ranges is prepared by stacking a wide band gap perovskite layer and a narrow band gap perovskite layer, the band gap range of the perovskite gradient band gap film is 1.35eV-2.3eV, and the perovskite gradient band gap film is matched with multiple scene applications. According to the spectral variation range of a light source, the perovskite polycrystal film superposition structure with a matched band gap is flexibly regulated and controlled, and then the optimal ambient light utilization efficiency is realized, and the efficient utilization of the spectrum in different application scenes is realized. The perovskite gradient band gap film prepared by using the layer-by-layer sequential deposition method has high crystallinity and good uniformity. By precisely regulating and controlling the thickness ratio of the evaporated lead halide and the organic salt, the annealing temperature and time, a multilayer high-quality perovskite film superposition structure with a gradient band gap is prepared.

The invention discloses a hyperspectral and LiDAR combined sub-pixel charting method considering multi-source features, belongs to the field of multi-modal image processing, and aims to solve the technical problems of category confusion and difficult sub-pixel distribution rule fitting caused by spectral variation of hyperspectral data in an existing sub-pixel charting method. Designing a multi-source sub-pixel charting data set manufacturing process, and generating low-resolution hyperspectral data, a low-resolution abundance graph, high-resolution LiDAR data and a high-resolution ground truth value; and then, constructing a multi-source feature embedded network comprising an abundance feature extraction branch, a hyperspectral feature extraction branch, an up-sampling module, a LiDAR feature extraction branch and a classifier, and realizing end-to-end mapping from a low-resolution image to a high-resolution sub-pixel mapping result under multi-source data collaboration. The method has the beneficial effects that by introducing the high-resolution LiDAR data to supplement the category distinguishing attribute and the spatial form information, the precision and reliability of sub-pixel mapping are effectively improved.