6 results about "Vegetation classification" patented technology

[Problem] To provide a vegetation determination system that estimates vegetation proportions of a plurality of plant classifications on the basis of a satellite image. [Solution] In the present invention, an information processing system generates a second trained model by using a training data set which includes a satellite image as input data and includes, as teacher data, an output result of a first trained model generated using a UAV image. The information processing system comprises: a vegetation proportion map generation unit that generates the first trained model by performing training processing for determining a vegetation classification, by using a high-resolution vegetation classification training data set in which the UAV image and the teacher data indicating the vegetation classification are associated with each other, and generates a vegetation proportion map on the basis of a vegetation classification image outputted from the first trained model; a low-resolution vegetation proportion training data generation unit that generates a low-resolution vegetation proportion training data set in which the vegetation proportion map is associated, as the teacher data, with the satellite image; and a second training processing unit that performs machine learning processing for determining vegetation proportions from the satellite image, by using the low-resolution vegetation proportion training data set.

The present application provides a kind of vegetation classification method, device, equipment and medium based on space-spectrum neural network, method includes: high spectral dataset is divided into training set, verification set and test set, and quantitative evaluation index is set;Based on the information separation of space-spectrum mixed search space, the differentiable architecture search strategy based on gradient optimization is used to search the neural network architecture, and the target network architecture is obtained, the information separation spectrum transformation operator for extracting spectral features is included in the space-spectrum mixed search space, and the information separation space attention depth convolution operator for extracting spatial features;Based on training set and verification set, the target network architecture is trained to obtain a classification model;Test set is input into the classification model to obtain vegetation classification result, and the classification result is evaluated according to quantitative evaluation index, solves the problem that classification accuracy and efficiency still have room for improvement in complex vegetation fine classification task, cannot meet the demand of large-scale, high-precision remote sensing monitoring.

Vegetation management system includes: a data acquisition unit that acquires input data including remote sensing data obtained by photographing, by remote sensing, a facility and vegetation to be analyzed; a vegetation classification unit that classifies the vegetation photographed in the remote sensing data; a wide-area growth prediction unit that predicts a time-series change of a growth range of the vegetation photographed in the remote sensing data; a vegetation amount simulation unit that predicts a fluctuation in the growth amount of each vegetation by a simulation; a three-dimensional construction unit that constructs a three-dimensional model expressing the facility and the vegetation; and a risk determination unit that determines a contact risk indicating a contact possibility between the facility and the vegetation.

The application discloses a vegetation species identification method for strengthening AI computing power and time sequence tracing, comprising the following steps: collecting multi-source remote sensing data and performing pretreatment, constructing a vegetation classification dataset with spatiotemporal alignment and unified resolution; generating a vegetation mask based on the vegetation classification dataset, obtaining a standardized sample slice, and constructing a training dataset in combination with spectral characteristics; performing time-phase processing on the training dataset based on vegetation phenological characteristics, and outputting a preliminary vegetation classification result; performing object-level optimization on the preliminary vegetation classification result, and obtaining an optimized vegetation classification result; correcting the optimized vegetation classification result in combination with terrain data, generating a vegetation species classification map of a target year, and realizing vegetation dynamic change inversion in a specified time period through transfer learning. Therefore, the traditional resolution limit can be broken through, the classification error problem caused by independent use of multi-source data can be solved, the discrimination of complex vegetation types can be significantly improved, and historical vegetation dynamic backtracking analysis can be supported.