75 results about "Spectral index" patented technology

An embodiment of the invention discloses a hyper-spectral estimation model constructing method of total nitrogen content of rice leaves. The method comprises steps as follows: multiple experimental plots are selected, and multiple sampling points are selected in each experimental plot; canopy spectral measurement is performed at the critical growing stage of rice; multiple sampling spectrums are recorded at each sampling point, and an average value is taken as a spectral measurement value of the sampling point; a hyper-spectral image of each experimental plot is acquired by an airborne imaging spectrometer; multiple function leaves at different parts are collected at each sampling point, and the total nitrogen content of the rice leaves is measured; the hyper-spectral estimation model of the total nitrogen content of the rice leaves is constructed with the adoption of spectral indexes or a partial least-squares regression method. The embodiment of the invention further discloses a hyper-spectral estimation method of the total nitrogen content of the rice leaves. The total nitrogen content of the rice leaves is estimated according to the model constructed with the method. The scientific and technical basis can be provided for space inversion of the nitrogen content of regional-scale rice and efficient implementation of precision agriculture.

The invention discloses an object-oriented remote sensing inversion method of a leaf area index of a crop, comprising the following steps of: acquiring multispectral remote sensing data; calculating a biomass spectral index NDVI (Normalized Difference Vegetation Index), a crop nutrient spectral index BRI and a water sensitive spectral index NDWI (Normalized Difference Water Index) of a crop colony by utilizing the acquired multispectral remote sensing data; carrying out object-oriented segmentation and encoding according to the biomass spectral index NDVI, the crop nutrient spectral index BRI and the water sensitive spectral index NDWI of the crop colony by utilizing a mean shift algorithm; sequentially carrying out the original spectral mean calculation of pixels on objects according to an encoding sequence to obtain a spectral index SAVI (Soil-Adjusted Vegetation Index) sensitive to the LAI (Leaf Area Index), and carrying out texture structure calculation; building a regression model of ground LAI observation data, the spectral index SAVI sensitive to the LAI and the texture structure calculation; and carrying out inversion calculation on the object without the ground LAI observation data by utilizing the regression model to obtain the LAI of the object without the ground LAI observation data.

The invention belongs to the technical field of agricultural remote sensing, and particularly relates to a Landsat remote sensing data-based multi-cloud rain and fog region rice identification method.The method includes: distinguishing rice from other obvious ground objects, and obtaining a potential rice planting area in a remote sensing image; based on ground measured data, extracting the relationship between the spectral indexes of the rice sample pixel data in the transplanting stage; establishing a vegetation index threshold value model for rice identification by combining rice plantingregion division data and rice potential transplanting period data, extracting rice planting areas in remote sensing images step by step in a segmented mode, combining the planting areas of different rice types, and obtaining a final rice planting region distribution diagram rapidly and accurately. Compared with an existing method for extracting the rice planting area through a remote sensing technology, the method is mainly suitable for southwest China regions with cloudy rain and fog. According to the invention, the actual planting condition can be truly reflected, and the planting area extraction precision is improved.

The invention relates to a chlorophyll content measurement method and an apparatus thereof. The method comprises the following steps: acquiring the chlorophyll content of a preset sample, and calculating a corresponding spectral vegetation index according to the hyperspectral reflectivity data of the preset sample; constructing a model of a predication relationship between the chlorophyll content of a vegetation to be measured and the spectral vegetation index according to the chlorophyll content of the preset sample and the corresponding spectral vegetation index; obtaining the hyperspectral reflectivity data of the leaf and canopy of the vegetation to be measured; calculating the spectral vegetation index of the vegetation to be measured by using a spectral index algorithm according to the hyperspectral reflectivity data; and inversing by adopting the pre-constructed predication relationship model according to the spectral vegetation index of the vegetation to be measured in order to obtain the chlorophyll content of the vegetation to be measured. An optical remote sensing technology is used in the chlorophyll content measurement field, so accurate, simple, fast and timely measurement of the chlorophyll content is realized, and the method and the apparatus are of great significance to monitor the growth vigor of crops and estimate the output.

The invention discloses a spectral index model-based black and odorous water body identification method and system. The identification method comprises the steps of processing a selected high-spatial-resolution remote sensing image, superposing a river system vector document or a mask file corresponding to the processed fused remote sensing reflectance image to the fused remote sensing reflectance image, extracting river in the fused remote sensing reflectance image to obtain a river remote sensing image; obtaining blue, green, red and near-infrared bands of each pixel in the river remote sensing image and calculating a black and odorous spectral index according to the bands and a preset spectral index model; and determining the category of the water body corresponding to each pixel according to the relationship between the spectral index of each pixel unit and preset first threshold and second threshold. By adopting the black and odorous spectral index model, the identified black and odorous spectral index of the urban river is more accurate, the categories of the water bodies of the rivers are divided into a heavy black and odorous water body, a light black and odorous water body and a general water body, and differentiation of the water bodies is more detailed.

Systems, computer-readable media, and systems are provided for determining whether a data point indicates a presence of a shadow-covered ground point. A data point from top of atmosphere data from an imaging study of an area potentially covered by a cloud shadow is selected. At least one spectral data measurement associated with the data point, the spectral data measurement including at least one of visible, near-infrared, and short wavelength infrared data is taken. At least one of the spectral data measurement and derived spectral index is compared with a spectral data threshold, the spectral data threshold delineating between a shadow-covered ground point and a non-shadow-covered ground point. The data point is classified as one of a shadow-covered ground point and a non-shadow covered ground point based on the comparison with the spectral data threshold.

The invention discloses a remote sensing estimation method for the SPAD value of rape leaves in different growth periods. The method comprises the following steps: by taking commercial crop rapes in northwest regions as research objects, measuring the spectral reflectivity and SPAD value of rape leaves in each growth period; analyzing the relativity between 10 types of spectral indexes and the SPAD value of the rape leaves; constructing an SPAD random forest regression estimation model of the rape leaves based on the spectral indexes, and verifying the constructed model; and comparing the constructed model with a traditional simple linear regression model and a traditional multiple stepwise regression model. Modeling and verifying R2 respectively reach 0.91 and 0.74 or above, the verifying RMSE is between 1.571 and 5.004, RE is between 2.66% and 13.22%, and thus an optimal estimation model of the SPAD of the rape leaves can be obtained.

The invention belongs to the technical field of crop growing management and discloses a regulation and control method for rice nitrogen topdressing based on a nitrogen spectral index method. According to the invention, the nitrogen spectral index method is constructed according to nitrogen amount required by target rice yield, apparent utilization rate of spike fertilizer, plant real-time nitrogen accumulation amount estimated based on reflectance spectrum and soil nitrogen supplying amount while nitrogen fertilization based on a nutrient balancing principle. According to the invention, in the aspect of real-time information acquisition, plant canopy group information is utilized, thus the regulation and control method disclosed by the invention has the quicker and lossless characteristics and the like, thereby avoiding the phenomenon of point replacing surface; in the aspect of a nitrogen topdressing regulating and controlling model, the soil nitrogen supplying amount and nitrogen fertilizer utilization rate during the middle-late period are quantified, thereby enhancing the mechanism characteristic and accuracy of nitrogen topdressing amount estimation and improving the quantification level.

The invention relates to an atmospheric correction and regional mineral map spotting method utilizing multi-scene ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) remote sensing data, comprising the following nine steps of: (1) reading in ASTER data; (2) selecting reference data, wherein one-scene data is selected from multi-scene data as a reference for atmospheric correction; (3) carrying out the atmospheric correction on the reference data to obtain reference reflectivity data; (4) carrying out atmospheric correction on non-reference data to obtain non-reference reflectivity data; (5) calculating the overlay region spectral characteristic difference of the reference reflectivity data and the non-reference reflectivity data; (6) calculating the atmospheric water vapor content and the atmospheric visibility of the non-reference data; (7) newly carrying out the atmospheric correction on the non-reference data by utilizing the calculated atmospheric water vapor content and the atmospheric visibility to obtain non-reference reflectivity data; (8) carrying out inlaying treatment on the multi-scene reflectivity data including the reference data and the non-reference data; and (9) carrying out mineral identification and map spotting by utilizing spectral indexes. The method effectively eliminates inlaid gaps among the multi-scene data, thereby realizing the regional mineral map spotting.

The invention provides a retrieval method and a retrieval device of chlorophyll a concentration of inland case II water, wherein the retrieval method of the chlorophyll a concentration of the inland case II water comprises the steps of firstly, classifying first remote sensing reflectance spectra to obtain a plurality of second remote sensing reflectance spectra taking different components as main components; calculating the weight matrixes of all the second remote sensing reflectance spectra, selecting all characteristic wave bands and calculating the chlorophyll spectral indexes; finally, carrying out weight fusion on the chlorophyll spectral indexes under all categories of all the spectra by weight to obtain weighted chlorophyll index, and fitting an inversion model to obtain the chlorophyll a concentration. Due to the method of firstly classifying and then carrying out weighted inversion, the limitation on the retrieval method caused by the area and the time can be reduced, and the smoothness and the stability of inversion results can be improved, and therefore, when the chlorophyll a concentration of a certain second remote sensing reflectance spectrum is inversed subsequently, the chlorophyll a concentration can be obtained by inversion through the inversion model corresponding to the second remote sensing reflectance spectrum, so that the universality of the method is improved.

The invention belongs to the field of crop growth monitoring, and discloses a method for monitoring the wheat plant water content (PWC) under different plant nitrogen content (PNC) levels. The method comprises the following steps: combining wheat canopy blade reflectance spectroscopic data with PWC data, carrying out classification according to different PNC size levels, determining a core waveband common region under different PNC levels, constructing an optimum spectral index based on the core waveband common region, and establishing a general monitoring model of the wheat plant water content on the basis of the optimum spectral index. According to the invention, the influence of low, medium and high nitrogen nutrition conditions to PWC monitoring is fully considered, the selected optimal spectral index is suitable for wheat PWC high-spectrum monitoring under multiple kinds of nitrogen nutritions, and the wheat PWC under different water nitrogen conditions can be rapidly and accurately estimated without loss. The method provides important technical support for high-spectrum monitoring on the wheat plant water content under different nitrogen nutrition conditions in the precision agriculture.

The invention relates to a vegetation change occurrence time detection method based on time series similarity, comprising: first, establishing multi-year time-space continuous vegetation index time series data of a research area, calculating JM distance between vegetation index time series curves of each past year and the initial year pixel by pixel, year by year, and generating a time series curve for the JM distance of each past year and the original year; fitting the time series curve for the JM distance of each past pear and the original year by using a logistic model, and acquiring time parameter from logistic model parameters so as to automatically extract vegetation change time. In the method, the JM distances of vegetation index time series curves between the past years and the initial year are used to indicate time series similarity, and vegetation change time is acquired from change law of yearly time series similarity. The method is effective in detecting changes of time series curves in terms of amplitude, frequency and the like, the complex step of decomposing original spectral index time series data is avoided, and the problem that it is difficult to extract indexes directly from original spectral index time series data to provide comprehensive characterization of vegetation changes is solved.

A method for remote sensing estimation of a weight ratio of green plants to withered plants of grassland comprises the following steps: acquiring grassland space distribution data of a research area; positioning ground sampling points; determining the weight ratio of green plants to withered plants at every sampling point; determining a spectral index for estimating the green plants to withered plants; determining a model for remote sensing estimation of the weight ratio of green plants to withered plants of the research area; calculating the spectral index to be estimated of the research area; carrying out remote sensing estimation on the weight ratio of green plants to withered plants, with the date to be estimated, of the research area; and carrying out dynamic remote sensing estimation researches on the weight ratio of green plants to withered plants of the grassland in the research area. The method allows a spectral waveband sensitive to the green plants and withered plants to be found through a remote sensing technology, constructs the spectral index for estimating the weight ratio of green plants to withered plants, realizes remote sensing estimation of a weight ratio of green plants to withered plants of the grassland, and provides technical support for ecologic researches and development strategy of the grassland.

The invention provides a method for structuring a wheat blumeriagraminis speer early detection model by extracting sensitive parameters on the basis of subwindow permutation analysis (SPA). The method comprises the following steps of 1) acquiring the hyperspectral reflectance of an infected wheat; 2) extracting a sensitive wave band from the original wave band of the hyperspectral reflectance through the SPA algorithm; 3) selecting spectral indexes possibly related to diseases in existing research, and extracting sensitive spectral indexes from the spectral indexes through the SPA algorithm; 4) through partial least square-linear discriminant analysis, taking the sensitive wave band or the sensitive spectral indexes as component input variables to structure the wheat blumeriagraminis speer early detection model; 5) testing the wheat blumeriagraminis speer early detection model through the dichotomy algorithm, evaluating model performance through leave-one-out cross validation on the basis of independent susceptible varieties. Therefore, a wheat blumeriagraminis speer sensitive spectrum extracted on the basis of SPA is accurate in characteristic and few in wave bands, and the structured wheat blumeriagraminis speer detection model is simple, high in accuracy and good in stability.

The invention discloses a fire burned area identification method adopting the spectral index and a neural network combined. Through analysis on spectral characteristics of different ground object classes, the spectral index with the optimal separability for all the ground object classes is adopted as the input characteristic of the neural network, and the distinction degree of the pixel of a burned area and other classes of pixels can be increased; and meanwhile, the spectral index serves as a training sample to construct a classifier, thus burned area identification can be conducted on a satellite image, and applicability and accuracy are improved.

The invention discloses a spectral index constructing method for detecting carbon nitrogen ratios of canopy leaves of crops. The method comprises the following steps: (1) obtaining hyperspectral original reflectivity data of crop canopies within the waveband range of 500-910 nm; (2) figuring out a relative reflectivity by using the obtained original reflectivities of the crop canopies; (3) figuring out gradients of a relative reflectivity spectrum curve within three sub waveband ranges of 500-550 nm, 680-760 nm and 760-910 nm; and (4) constructing a spectral index RCN for detecting the carbon nitrogen ratios of the canopy leaves of the crops. Through the adoption of the spectral index constructed according to the invention in detecting the carbon nitrogen ratios of the canopy leaves of the crops, the influences of lighting condition differences on the detection of the carbon nitrogen ratios can be eliminated and the problem that adjacent wavelength reflectivities of the existing spectral index are related in height can be also avoided.

The invention discloses a depth feature and graph cut method-based built-up area automatic extraction method, and belongs to the technical field of image data processing. The method comprises the steps of using a high-resolution full-color image and a multispectral image subjected to ortho-rectification and linear cutting stretching processing as data sources; dividing the full-color image into image blocks same in size; extracting depth features of the image blocks by using a deep convolutional neural network; by taking the image blocks as nodes and taking the depth features as node features,building a graph model; determining built-up areas by using a graph cut method; performing voting by taking the image blocks as primitives based on multiple spectral indexes of the multispectral image to remove a false alarm; eliminating the built-up areas with the excessively small areas and non-built-up areas; performing super-pixel segmentation on the image blocks of built-up area edges; performing the voting based on the multiple spectral indexes of the multispectral image to remove the false alarm, thereby obtaining refined built-up area edges; and finally extracting an edge vector graph. The method can quickly, effectively and accurately realize the extraction of the built-up areas.

The invention relates to an urban green land extracting method cooperating with a multisource remote-sensing image. The method comprises the following steps that S1, a high-resolution image and a multispectral image are collected, image registering is carried out, the registered images are stacked, and a stacked image is obtained; S2, the stacked image is subjected to image segmentation based on the ground object space-spectrum features of the stacked image in S1, and a final segmented object is obtained; S3, vegetation spectral indexes are constructed through spectral information in the multispectral image based on the final segmented object in S2, a threshold value is selected, urban green land information is obtained, and drawing is carried out. Compared with the prior art, the urban green land condition can be extracted fast and accurately, drawing is carried out, and the method can be widely applied to the fields of urban planning, urban environment assessment and the like and isbeneficial to urban planning and development of the environmental protection cause.

The invention discloses a method for predicting the content of winter wheat grain protein based on coupling of spectral indexes and climatic factors, and a method for establishing a prediction model, aiming at solving the technical problems of low accuracy and universality of a current model for predicting the content of winter wheat grain protein. The method for establishing the model comprises the following steps: acquiring information; determining a light temperature accumulation index; establishing a light temperature nitrogen index; determining a best spectrum parameter; establishing a prediction model; and checking the model. Through the method, the model for predicting the content of the winter wheat grain protein is high in accuracy and good in universality; the expansibility and the stability are improved; the method is capable of effectively predicting the change of the content of the winter wheat grain protein under different cultivation and management conditions.

The invention discloses a method for determining a summer maize canopy SPAD value estimation model based on hyperspectral technology. The method comprises the following steps: establishing a quantitative relationship model among the summer maize canopy SPAD values, a sensitive band and a spectral index by monitoring and analyzing data such as a plant canopy hyper-spectrum and plant canopy leaf SPAD values, analyzing change characteristics of the summer maize plant canopy spectral reflectivity under different SPAD values by the test of a small-scale lysimeter, determining a summer maize plant canopy original spectrum and a response relationship between a first-order differential and the canopy SPAD values, and screening sensitive bands and the optimal spectral index of the summer maize canopy SPAD values monitored during the whole growth period, thereby promoting the application of hyperspectral technology in accurate management and rapid and non-destructive growth monitoring of summermaize water and fertilizer.