13 results about "Forest classification" patented technology

The embodiment of the invention discloses a camellia oleifera forest classification method, device and equipment and a medium. The method comprises the steps that a remote sensing image and topographic data of a detection area are acquired, the remote sensing image comprises time sequence spectral data, and the time sequence spectral data represents spectral characteristics of the earth surface of the detection area; based on a pre-trained classification model, first classification information of the camellia oleifera forest in the remote sensing image is determined according to the remote sensing image, phenological characteristics and topographic data, the phenological characteristics represent the phenological stage of the camellia oleifera, and the phenological stage is determined based on the growth cycle of the camellia oleifera; the classification model is obtained by training a data set formed by combining a remote sensing image sample corresponding to the sampling area with topographic data and phenological characteristics; determining a target area in the remote sensing image based on the first classification information, determining a first classification parameter of the target area based on the time sequence spectrum data of the target area and the weight of the phenological feature, and determining second classification information of the target area based on the first classification parameter. According to the technical scheme provided by the invention, the classification capability and the classification accuracy of the camellia oleifera forest can be improved.

The invention discloses an intertidal zone identification method and related equipment, and belongs to the technical field of remote sensing identification, and the method comprises the steps: obtaining a remote sensing image of a target area; fitting different wavebands of the remote sensing image based on the pixels to obtain a time sequence fitting curve; based on the time sequence fitting curve, selecting a long time sequence training sample and a verification sample for training a random forest classification model; and performing classification identification on the remote sensing image by using the trained random forest classification model to obtain an intertidal zone identification result of the target area. According to the method, the long-time sequence samples are extracted on the basis of taking the pixels as units, the intertidal zone range is extracted by fully applying the information between the long-time sequence images, and the efficiency, precision and flexibility of extracting the intertidal zone information of the long-time sequence images are effectively improved.

The invention relates to a leaf area index time sequence processing method, which comprises the following steps of: inputting a time sequence remote sensing image and suburb forest and economic forest classification data of the same region; calculating a normalized differential vegetation index of the research area and carrying out time sequence sorting; calculating vegetation coverage data of the research area and carrying out time sequence sorting; synthesizing to obtain monthly FVC time sequence data of the suburb forest region and the economic forest region; calculating a suburb forest leaf area index LAI in the research area and performing time sequence sorting; calculating the economic forest leaf area index LAI of the research area and performing time sequence sorting; performing seasonal decomposition on the suburb forest time sequence data after adaptive filtering; performing seasonal decomposition on the economic forest time series data after adaptive filtering; merging the suburb forest time sequence data and the economic forest time sequence data; and outputting to obtain final optimized data. The invention further relates to a leaf area index time sequence processing system. According to the invention, a purification LAI time sequence product which clearly represents long-term trend, mutation and gradual change signals of respective vegetation canopy structures can be output.

This invention discloses a method and system for identifying planted and natural forests based on multi-source remote sensing and spatiotemporal joint optimization, belonging to the fields of remote sensing image processing, earth observation, and dynamic monitoring of forest ecology. Addressing the problems of high label noise and disordered temporal logic in existing technologies, this invention obtains high-purity training samples through multi-stage iterative purification by fusing CCDC mutation history and deep semantic features; obtains pixel-level confidence through independent initial classification by geographic partitioning; constructs an HMM model integrating ecological prior constraints and dynamic adaptive emission probabilities for Viterbi decoding; and performs constrained spatial restoration with cross-dimensional temporal security checks. This invention effectively suppresses temporal pseudo-variables and salt-and-pepper noise, achieving high-resolution thematic mapping year by year with no temporal logical contradictions and regular spatial patches, suitable for fine-grained forest classification and compliance monitoring of zero-deforestation laws in tropical and subtropical regions.

This application presents a method for classifying land features along railway lines using remote sensing imagery based on morphological entropy fusion. The method includes: acquiring Sentinel-1 radar imagery, Sentinel-2 optical imagery, and a fused image of the target area from a GEE platform, and preprocessing the data; generating masks on the Sentinel-1, Sentinel-2, and fused images respectively, and calculating and extracting circumferential morphological entropy, radial morphological entropy, and joint morphological entropy; extracting polarization and texture features from the Sentinel-1 imagery, and spectral and exponential features from the Sentinel-2 imagery, and constructing corresponding composite features on the fused imagery; combining these features with morphological entropy features from corresponding data sources; training a random forest classification model based on each feature set, and using the trained model to classify four typical land features along the railway line: buildings, railways, vegetation, and highways. This method, by fully utilizing multi-source remote sensing information, effectively characterizes the structural complexity and irregularity of land feature outlines through morphological entropy, significantly improving the accuracy and reliability of the classification results.

The invention relates to an urban vehicle-mounted point cloud forest tree classification method based on RoPE operator local feature aggregation, and belongs to the technical field of photogrammetry and remote sensing. According to the method, the ROPE operator technology is adopted, the relative coordinates and the original features of all points in the neighborhood are coded to establish the weight relation, a model is helped to distinguish the fine features of the trunk, the coding operator is utilized to carry out secondary local feature dynamic aggregation, the local structure and context information of the forest are captured, and the problem that the classification precision of the trunk and the shielding forest is low is solved; meanwhile, a U-shaped encoder-decoder network architecture and a jump connection strategy are introduced, forest features are fused among different levels, and multi-scale forest information is obtained. Compared with other methods, the method can solve the problem that tree classification errors of tree trunks and shelters often occur in urban tree classification, and realizes accurate segmentation of complete tree information in a complex urban scene.

This invention, entitled "A Method, System, and Storage Medium for Remote Sensing Estimation of Carbon Storage Based on Forest Heterogeneity," belongs to the field of carbon storage estimation technology. The technical problem it aims to solve is the poor accuracy of regional carbon storage estimation due to insufficient ground sample quantity and uneven spatial distribution. Key technical points include: S1, expanding the limited ground carbon storage samples using spaceborne lidar photon point cloud data to construct a ground-space fusion forest carbon storage measured sample library; S2, interpreting remote sensing data of the target area's forest ecosystem and classifying forest vegetation types, considering forest carbon sink heterogeneity and employing continuous change detection and classification algorithms for forest vegetation type classification; S3, constructing a carbon storage remote sensing estimation model coupling forest classification and multidimensional features; and S4, performing carbon storage estimation based on the carbon storage remote sensing estimation model.

The invention provides an object-oriented natural forest and artificial forest classification method, and relates to the field of forest classification and recognition research, and the method comprises the steps: carrying out the superpixel segmentation of a preprocessed Sentinel-2 image, and obtaining an optimal segmentation scale; obtaining a multi-source remote sensing product; the method comprises the following steps: based on a Sentinel-2 image and a multi-source remote sensing product, taking an object as a basic unit, and extracting object-oriented multi-type features; performing feature optimization on the multi-type features; classifying the optimized features by applying a random forest algorithm and a support vector algorithm respectively, comparing the classification precision of the two methods, and determining an optimal classification model; obtaining a to-be-classified remote sensing image; and inputting a remote sensing image to be classified into the trained optimal classification model to obtain an optimal classification result of the natural forest and the artificial forest. According to the technical scheme, the problem that natural forests and artificial forests are difficult to classify is solved.

The invention discloses an electromagnetic wave signal random forest classification method and device, equipment and a medium. The method comprises the following steps: acquiring and preprocessing an original electromagnetic wave time domain discrete signal sequence to obtain preprocessed signal data; based on the preprocessed signal data, positive and negative pulse event data are extracted through an adaptive threshold pulse detection algorithm; the positive and negative pulse event data comprises a positive pulse number, a negative pulse number and a total pulse number; determining a polarity characteristic, an average frequency characteristic and an average pulse width characteristic of the preprocessed signal data according to the positive and negative pulse event data; forming a feature vector by the total pulse number, the polarity feature, the average frequency feature and the average pulse width feature; and inputting the feature vector into a pre-trained random forest classification model, and outputting a classification result of the electromagnetic wave signal.

One example method includes receiving a random forest classifier model that comprises a group of decision trees, wherein the random forest classifier model is created using a vertical federated framework, providing new observations, not included in a set of training observations, to a trained random forest classifier model, wherein the random forest classifier model is trained in the vertical federated framework, and wherein the training is performed using the set of training observations as input to the random forest classifier model, and generating, by the trained random forest classifier model, one or more diversity scores pertaining to the new observations.

The invention relates to a vegetation coverage time sequence processing method, which comprises the following steps of: inputting a time sequence remote sensing image and suburb forest and economic forest classification data of the same region; calculating a normalized differential vegetation index of the research area and carrying out time sequence sorting; calculating vegetation coverage data of the research area and carrying out time sequence sorting; synthesizing to obtain monthly FVC time sequence data of the suburb forest region and the economic forest region; adaptive filtering is carried out on the suburb forest time sequence data; carrying out seasonal decomposition based on a fixed period on the filtered suburb forest time sequence data; filtering the economic forest time series data; carrying out adaptive seasonal decomposition on the filtered economic forest time series data; combining the suburb forest time series data and the economic forest time series data obtained through decomposition; and outputting the merged time series data to obtain final optimized data. The invention also relates to a vegetation coverage time sequence processing system. According to the method, a reliable data basis can be provided for high-precision change detection and attribution analysis.

The application discloses a PUE prediction method and device of a data center and a storage medium, relates to the field of data center energy consumption, and is used for accurately and quickly predicting the PUE of the data center. The method comprises the following steps: inputting a to-be-predicted PUE sample at a first moment into a deep forest classifier for sample analysis, obtaining an analysis result, wherein the analysis result comprises N probability values, each probability value is used for representing the probability that the to-be-predicted PUE sample belongs to one of N preset categories, and N is a positive integer; and training an RDPG model based on the analysis result, a first historical PUE data set and environmental data at the first moment, obtaining a trained RDPG model, and the trained RDPG model is used for predicting the PUE value of the data center.

This invention provides an environmental quality detection method and apparatus, relating to the fields of image data processing and environmental quality detection, to address problems such as low detection accuracy and limitations. The method includes: preprocessing a hyperspectral remote sensing image to obtain an enhanced image; performing dimensionality reduction on the enhanced image using principal component analysis to obtain a first feature map; calculating the attention score of the first feature map, which is used to highlight target features in the dimensionality-reduced feature map; extracting features from the dimensionality-reduced feature image based on the attention score to obtain an attention feature map; performing multi-scale convolution on the first feature map to obtain second feature maps at different scales, and then performing a weighted summation to obtain a third feature map; fusing the attention feature map and the third feature map, and inputting the resulting fourth feature map into a random forest classification model to predict the quality detection result, wherein the weight of the decision tree that can accurately classify minority class samples in the random forest classification model is greater than the weight of other decision trees.