Abstract: A method for extracting a black-odorous water body based on a CART classification model includes: selecting a research region, designing sampling points within the region; monitoring relevant chemical indicators of the water body at various sampling points, extracting remote sensing reflectance data of the water body, determining a type of the water body according to a classification standard of relevant chemical indicators for an urban black-odorous water body; comparing and analyzing the remote sensing reflectance data to obtain spectral change features of the black-odorous water body and a general water body; constructing each node of a decision tree according to the spectral change features and based on Gini index, constructing a decision tree classification model to obtain classification results of the black-odorous water body and the general water body, calculating a classification accuracy; analyzing the classification results to obtain spatiotemporal distribution changes of black-odorous water bodies

Abstract: A spectral information separation and aggregation method includes: performing smooth denoising on spectral data through a spectral denoising method to obtain processed spectral data; processing the processed spectral data through a spectral data processing method to obtain spectral information with at least two dimensions and thereby spectral information of multi-dimensions are obtained; quantitatively analyzing correlations among spectral information of respective dimensions through a correlation-analysis algorithm, and calculating spectral information coupling coefficients according to the correlations among the spectral information of the respective dimensions; coupling the spectral information of multi-dimensions to thereby obtain resultant spectral information; and calculating a correlation between the resultant spectral information and ground-object parameters through a correlation-analysis algorithm, and evaluating an effect of the coupling.

Abstract: The present invention discloses a CEEMDAN-based method for screening and monitoring soil moisture stress in farmland, characterised by the steps: preprocessing of remote sensing images, construction of NDVI long time series, CEEMDAN decomposition, calculation of statistical descriptors, screening of soil moisture stress sequences, ground data measurement, construction of soil moisture stress characteristic curves, fitting of soil moisture stress response characteristic curves and predicting the content of soil moisture stress. The invention adopts CEEMDAN decomposition, which solves the problems of noise residue and low reconstruction accuracy in the previous methods, and the high reconstruction accuracy of decomposed component data is more conducive to capturing the transient effects of soil moisture stress, and realizes the screening and extraction of soil moisture stress by combining with the ground measured data.

Abstract: A multi-scale aggregation pattern analysis method for a complex traffic network is provided, which belongs to the field of the highway traffic network. Firstly, an adjacency matrix, a position attribute matrix, a distance weight matrix, a road grade matrix, and a time-phased traffic congestion degree matrix of a highway traffic network are calculated; secondly, a weight influence factor of the road network is incorporated based on a PageRank algorithm to determine order of critical nodes; finally, a two-dimensional decision diagram is drawn by two indicators: order of critical nodes and a shortest path distance. A new weighting matrix which accords with the actual situation of the road network is obtained by incorporating a position weight matrix, a distance weight matrix, a road grade weight matrix and a dynamic traffic congestion degree weight matrix based on a similarity matrix of the spectral clustering.

Abstract: The present disclosure provides a data compression method for quantitative remote sensing with an unmanned aerial vehicle. The method performs preprocessing on a multispectral image acquired by an unmanned aerial vehicle, successively performs a three-dimensional convolution and a two-dimensional convolution on the multispectral image by an encoder to obtain deep feature information, performs quantizing and entropy encoding on the deep feature information, optimally distributes a loss and a code rate of the image through end-to-end joint training to obtain an optimal compressed image, and reconstructs the optimal compressed image by using a decoder. Image reconstruction quality and a compression ratio are improved by performing a plurality of convolutions on a multispectral pattern; quantizing and entropy encoding are performed on the convoluted deep feature information, to remove redundancy in a feature image, so as to improve the image reconstruction quality and the compression ratio.

Abstract: The present invention discloses an automatic interpretation method for winter wheat based on a deformable fully convolutional neural network (FCN). The method includes the following steps: step 1: using a region corresponding to a winter wheat planting area as a research region, and obtaining a high-resolution remote sensing image of the research region; step 2: preprocessing the obtained remote sensing image: extracting geometric changes of sizes, fields of view, postures, and partial deformation of winter wheat objects in different spatial positions in the high-resolution image as sample data; step 3: establishing the deformable FCN; step 4: inputting the sample data to the deformable FCN to implement training of the deformable FCN; and step 5: inputting a to-be-recognized remote sensing image of winter wheat to the trained deformable FCN, and outputting a prediction graph of a winter wheat planting area in a to-be-recognized region.

Abstract: The present invention discloses an automatic interpretation method for winter wheat based on a deformable fully convolutional neural network (FCN). The method includes the following steps: step 1: using a region corresponding to a winter wheat planting area as a research region, and obtaining a high-resolution remote sensing image of the research region; step 2: preprocessing the obtained remote sensing image: extracting geometric changes of sizes, fields of view, postures, and partial deformation of winter wheat objects in different spatial positions in the high-resolution image as sample data; step 3: establishing the deformable FCN; step 4: inputting the sample data to the deformable FCN to implement training of the deformable FCN; and step 5: inputting a to-be-recognized remote sensing image of winter wheat to the trained deformable FCN, and outputting a prediction graph of a winter wheat planting area in a to-be-recognized region.