Abstract: Based on ICESat-2 high-resolution data, the disclosure proposes a method for mapping tree height.

Abstract: A semantics-based high resolution reconstruction method of a nighttime light remote sensing image includes: constructing a sample data set; the sample data set includes a plurality of data groups, and each data group includes a LR NTL image, and a HR NTL image and light semantics information consistent in spatial position with the LR NTL image; constructing a reconstruction model; performing training and validation on the reconstruction model by using the sample data set to obtain an optimized reconstruction model; and taking a to-be-reconstructed LR NTL image and light semantic information corresponding to the to-be-reconstructed LR NTL image as an input of the optimized reconstruction model, and outputting, by the optimized reconstructed model, a HR NTL image obtained through resolution reconstruction.

Abstract: A method for decoding and encoding network steganography includes: extracting an attention mask of a container image by a convolutional block attention network; extracting two-dimensional image features of a secret image by a feature preprocessing network; splicing the two-dimensional image features and the attention mask of the container image and the secret image in a channel layer, and inputting a spliced image into an encoding network to generate a stego image; inputting the stego image and the container image into a decoding network to respectively obtain a reconstructed secret image and a generated secret image; and constructing a total loss function considering a similarity between the container image and the stego image, a similarity between the secret image and the reconstructed secret image, and a difference between the reconstructed secret image and the generated secret image, and thus performing training on a network model.

Abstract: A large-scale forest height remote sensing retrieval method includes: acquiring Ice, Cloud and land Elevation Satellite (ICESAT-2) tree height data, Landsat data, Shuttle Radar Topography Mission (SRTM) data, Worldclim data, forest type data and ecological zoning data within a target zone, and preprocessing the data; carrying out georeferencing on the processed data to generate a first data set; calculating spectral features, terrain features and climatic factor features of an image, and combining the calculated features with the ecological zoning data and the forest type data to obtain a second data set; extracting eigenvalues of a same geographical location from the second data set, and combining the extracted eigenvalues with the tree height data to generate training data; constructing a random forest model covering a large zone as an ecological zoning tree height retrieval model, and dividing the obtained training data into a training sample and a verification sample.

Abstract: A method for retrieving heights of densely-covered forest canopies near power transmission lines includes: acquiring ICESat-2 LiDAR data, JL-1 image data, auxiliary data and three-dimensional information data about the power transmission lines within a target area; carrying out image preprocessing on a JL-1 image to generate a first image; screening the ICESat-2 LiDAR data according to a screening rule to obtain high-quality laser tree height data; by employing the first image, the high-quality laser tree height data and the auxiliary data, training a neural network model for retrieving a forest tree height according to an optical image and the auxiliary data; by employing the neural network model, generating a height distribution map of densely-covered trees in the target area; and calculating a height difference between a tree and a power transmission line nearest the tree to generate a hidden danger troubleshooting theme map.

Abstract: An automatic precision calculation method of a pose of a rotational linear array scanning image includes: obtaining a collection parameter of the rotational linear array scanning image and a camera intrinsic parameter; based on the above parameters, projecting the rotational linear array scanning image to its tangent plane by orthographic projection transformation to generate an equivalent frame image having the approximately same intrinsic and extrinsic parameters as the rotational linear array scanning image and calculate a coordinate of each pixel of the equivalent frame image projected onto the rotational linear array scanning image based on an inverse projection transformation calculation method; by using structure-from-motion method, automatically calculating a pose parameter of the equivalent frame image and a corresponding waypoint three-dimensional coordinate; with the pose parameter of the equivalent frame image as an initial value, obtaining an accurate imaging parameter of the rotational linear ar

Abstract: The disclosure provides a method of analyzing an influence factor for predicting a carbon dioxide concentration of any spatiotemporal position. Firstly, an atmospheric carbon dioxide spatiotemporal distribution simulation method is proposed. This simulation method constructs a simulation model simulating carbon dioxide concentration distribution of any position of a region based on machine learning algorithm in combination with carbon dioxide data of satellite observation and corresponding environmental factors; next, by use of a global sensitivity analysis method, quantitative evaluation on the importance of multiple influence factors for regional carbon dioxide distribution is achieved.