85 results about "Terrain classification" patented technology

The invention relates to the technical field of land resource management, in particular to a land natural resource dynamic supervision system and method. The method comprises the following steps: carrying out multi-source data acquisition and preprocessing on a land natural resource area to obtain an initial data set; performing intelligent land classification processing according to the initial data set to obtain a land classification map; performing change detection graph extraction according to the land classification graph to obtain a change detection graph; constructing a three-dimensional land resource model according to the change detection graph to obtain the three-dimensional land resource model; and performing early warning trigger processing according to the three-dimensional land resource model and the change detection graph, and pushing resource supervision information so as to realize a dynamic supervision task of the land natural resources. According to the land natural resource dynamic supervision method based on multi-source data, deep learning, time sequence analysis and three-dimensional modeling, automation, intellectualization and dynamics of land resource supervision are achieved.

The invention belongs to the technical field of remote sensing semantic segmentation information extraction, and particularly relates to a remote sensing image land classification method based on SAM multi-order fine tuning. According to the method, remote sensing multi-classification precision and generalization ability can be improved. Firstly, a sample data set is constructed through visual interpretation based on domestic high-resolution series images; on the basis, a network is designed by utilizing the strong feature extraction capability of an SAM encoder, a domain offset adaptation module and a deep low-rank adaptation module are designed, a mask generation part of an original SAM is improved, and three-stage adaptation fine adjustment is performed on the SAM, so that the feature extraction capability of the network on a remote sensing image is enhanced, and the model performance is improved. In application, the remote sensing land utilization coverage multi-classification technology with practical value is obtained so as to practically promote application and development of a large model in remote sensing image semantic segmentation multi-classification.

The embodiment of the invention provides an AI analysis method and system applied to land resource investigation, and the method comprises the steps: obtaining the multi-source remote sensing image data of a target region, analyzing the spectrum and texture features of the multi-source remote sensing image data, generating a comprehensive land feature map, obtaining an initial land classification map through a land classification model, and obtaining the initial land classification map. And performing time sequence comparative analysis in combination with historical data to determine a dynamic area of land coverage change and generate a land resource change report. The method further comprises the functions of soil quality evaluation, change area visualization, abnormal area real-time monitoring, land classification model training, classification model adjustment according to user feedback and the like, and the efficiency and accuracy of land resource investigation are improved.

The invention discloses an SAR image and optical image deep fusion ground object classification method based on self-supervised learning, and aims to solve two challenges in remote sensing image classification: firstly, reducing dependence on a large amount of label data, and secondly, improving multi-modal data classification precision. The modal features of an SAR image and an optical image are extracted through independent network branches, deep feature extraction is performed in combination with a convolutional neural network and a double-branch structure of an attention mechanism, and unique advantages and complementary information of the two modals are fully utilized. By constructing a self-supervised learning loss function, the model is trained without label data; then, the trained feature encoder is migrated to a downstream ground feature classification task, and only a small number of marked samples are used for fine adjustment of the model; according to the method, complementary information among different modes is integrated, dependence on manual annotation of remote sensing images is reduced through a self-supervised learning method, and the precision and robustness of ground feature classification are improved.

The invention discloses a terrain classification method based on a random forest algorithm, a server and a storage medium, and belongs to the technical field of terrain classification, and the method comprises the steps: obtaining DEM data and remote sensing image data of a research area, and extracting terrain features, spectral features, index features and texture features; designing a feature combination scheme, and generating an optimal feature combination data set as input data of terrain classification; calculating an optimal parameter combination of a random forest algorithm, and generating an optimal random forest classifier; establishing a terrain classification system suitable for the research area, and constructing a terrain classification training sample set and a verification sample set; performing terrain classification by using a random forest classifier and the optimal feature combination data set to obtain a terrain classification result of the research area; and calculating and evaluating the precision of a terrain classification result by utilizing a verification sample set and a Kappa coefficient evaluation method. By adopting the method, the terrain classification refinement degree and the terrain classification calculation efficiency and classification efficiency can be improved.

The invention discloses a spectrum-space depth fusion hyperspectral image classification method oriented to a small sample condition, and the method comprises the steps: firstly carrying out the multi-scale hole convolution processing of input hyperspectral data through a range attention convolution SAC module, and extracting the multi-scale context features; then, a spatial normalization attention SNA mechanism is utilized to carry out adaptive weighting adjustment of spatial dimensions on the feature map, and spatial feature representation of the key area is enhanced; the method comprises the following steps: constructing a lightweight hybrid expert model LMOE, carrying out parallel processing and gating weighting through a multi-path expert network, carrying out efficient refining and mapping on features, finally fusing processed spectral features and spatial features, and carrying out pixel-level prediction through a classifier to obtain a terrain classification result map of a hyperspectral image. The method solves the problems that in the prior art, overfitting is prone to occurring under the small sample condition, the spectrum-space collaborative modeling capacity is insufficient, the long-range dependence obtaining efficiency is low, and the recognition precision is reduced under the class imbalance scene.

The invention discloses a foot-ground contact force estimation and terrain classification method of an ostrich-imitating robot. The method comprises the steps that a foot-ground contact force mapping matrix of the ostrich-imitating robot is constructed, force-position hybrid control is carried out, foot-ground contact force is obtained according to the matrix and input torque of a plate spring to a passive joint, and terrain classification information is obtained after the ground contact state is judged; establishing a terrain classification fusion model based on a weighted voting mechanism, and training the model according to terrain classification information; and performing terrain classification according to the terrain classification information and the trained model. The foot-ground contact force is estimated through the joint corner information of the robot, no extra sensor needs to be installed, real-time and accurate foot-ground contact force estimation and contact state obtaining are achieved, the problem that the performance of an ostrich-imitating robot system is reduced or fails due to the fact that a force sensor is prone to being damaged is effectively solved, system configuration is simplified, and the system reliability is improved. Foot-ground contact force estimation and accurate terrain classification of the ostrich-imitating robot can be completed without depending on an external sensor.

The invention provides a surface deformation monitoring and classifying method based on InSAR and deep learning, which comprises the following steps: (1) collecting multi-scene time sequence SAR images and DEM data of a monitoring area, (2) carrying out registration, interference and DEM difference on the multi-scene time sequence SAR images, (3) selecting PS pixels by using amplitude deviation and a coherence coefficient threshold value, (4) linking the PS pixels to construct a spatial network, and (5) classifying the spatial network. (6) recovering deformation information of PS monitoring points; (7) performing primary classification on registered SAR images by using a random forest; (8) performing secondary classification on the SAR images by using primary classification samples and a ViT network; and (9) performing classification, statistics and cause analysis on deformation based on a multi-classification result. According to the method, the classification precision of the SAR image classification method is improved, and the InSAR deformation can be effectively classified and analyzed by using the land classification result.

The invention provides a submarine topography classification method and device, electronic equipment and a storage medium, and relates to the technical field of image processing.The submarine topography classification method comprises the steps that backscattering data and water depth data corresponding to a target submarine area are input into a first modeling network and a second modeling network respectively, extracting a context feature of the backscattering data through a first modeling network to obtain a first modal feature, and extracting a context feature of the water depth data through a second modeling network to obtain a second modal feature; fusing the first modal feature and the second modal feature through a modal shuffling network to obtain a fused feature; performing enhancement processing on the fusion features through an adjacent matrix in the graph transformer network to obtain enhanced features; performing up-sampling processing on the enhanced features through an up-sampling network to obtain target features; and determining a terrain classification result of the target seabed area according to the target features. According to the method, the classification precision of the submarine topography and landform is remarkably improved.

The invention relates to the technical field of land classification, in particular to a land utilization classification method based on a space-time fusion technology, which comprises the following steps: firstly, acquiring original remote sensing images, classifying the original remote sensing images, and then respectively creating buffer areas for the divided original remote sensing images; the method comprises the following steps of: acquiring an original remote sensing image, dividing a buffer area into buffer area groups according to the category of the original remote sensing image, carrying out space-time fusion on different buffer area groups through a space-time fusion algorithm, splicing the fused image to generate a final image, and finally carrying out land utilization classification by adopting a supervised classification method according to the final image and a historical image. According to the method and the device, an effective solution is provided for improving the land utilization classification precision by integrating the time dynamic information of the low-resolution image and the space details of the high-resolution image based on the space-time fusion technology.

The invention discloses an idle land recognition method, device and equipment and a storage medium, and belongs to the technical field of machine learning, and the method comprises the steps: obtaining a first land feature set based on the land text data of a plurality of pieces of land; screening the first land feature set to obtain a second land feature set; semi-supervised learning cooperative training is carried out on a first classifier and a second classifier based on the second land feature set, the first classifier and the second classifier are used for idle land identification, the first classifier is realized based on a rotating forest algorithm, and the second classifier is realized based on an extreme gradient lifting algorithm; wherein in the semi-supervised learning cooperative training process, the prediction labels are screened based on a preset idle land classification rule. The method can realize accurate and efficient idle land identification.

The invention provides a quadruped robot terrain classification method and system based on sound feature and IMU information fusion, and relates to the field of terrain perception, and the method specifically comprises the steps: collecting an audio signal, processing the audio signal to obtain a Mel spectrogram, collecting an IMU signal, drawing a triaxial IMU signal time domain graph, dividing the time domain graph into a plurality of IMU information graphs, and obtaining a quadruped robot terrain classification result. The feature value of each time domain graph window is calculated, an IMU information graph corresponding to the Mel-frequency spectrogram in time relation can be established, then the Mel-frequency spectrogram and the IMU information graph are spliced according to the time sequence, and the spliced Mel-frequency spectrogram and IMU information graph are input into a neural network model for terrain classification; according to the method, the classification capability of the unstructured outdoor terrain is enhanced in a mode of fusing sound perception and ontology perception, and the spliced input image not only contains audio features, but also retains IMU information, so that a neural network model can learn from two different perception sources, terrain features can be comprehensively understood, and the classification efficiency of the unstructured outdoor terrain is improved. Therefore, the recognition capability of the complex environment is improved.

The invention relates to the technical field of land type classification, in particular to a land type classification method based on territorial resource data, and the method comprises the following steps: S1, data preparation; s2, determining a classification target, a classification standard and a classification range; s3, land feature extraction and analysis; s4, data preprocessing and cleaning; s5, land feature extraction and selection; s6, formulating a classification method and algorithm selection; s7, land type division and marking; s8, determining a classification method and technology; and S9, formulating a classification standard and index system. Through land feature extraction and analysis, data preprocessing and cleaning and land feature extraction and selection, land data diversity understanding and effective management can be achieved, classification precision and efficiency are improved, and it is ensured that the classification result is more accurate and reasonable through verification and precision of the classification result; the reasonable utilization and protection level of land resources is improved through result optimization, and the accuracy and applicability of land classification are improved.

The invention discloses a GIS-based land classification depth measurement method and system, relates to the technical field of land classification depth measurement, and aims to solve the problems that in traditional land depth measurement work, due to the fact that the number of measurement points is limited and distribution is lack of systematicness, the real change condition of land depth is difficult to comprehensively and accurately reflect, local depth abnormal areas are easy to omit, and the measurement accuracy is poor. According to the method, multi-source geographic data are integrated through the GIS technology, land depth information can be comprehensively and accurately reflected, and the problems that traditional measurement is low in precision, measurement point distribution is lack of systematicness, and data processing and analysis capacity is weak are solved. In the aspect of region division, the consistency of land features in the region is ensured by integrating a multi-factor consideration method, a dynamic adjustment mechanism of measurement point layout can be flexibly arranged according to the actual condition of the land, a slope calculation and depth estimation method is combined, the complex relation between geographic elements is fully considered, and estimation errors are effectively reduced.

The invention discloses a terrain classification and self-adaptive gait switching method for a sea-turtle-imitating amphibious robot based on visual tactile perception. The method comprises the following steps: establishing a robot model carrying a pressure sensor and a waterproof camera; the method comprises the following steps: acquiring tactile time sequence data and visual images in various terrains such as broken stones and sand beaches, and constructing a training data set; establishing a multi-modal convolutional neural network, and fusing features through dual-channel feature extraction and an adaptive attention mechanism; motion energy consumption and speed under each terrain are collected, and the optimal gait is determined; and constructing a gait switching finite-state machine in combination with terrain physical attribute classification and the optimal gait. According to the method, a sensing-decision-driving closed loop is formed, the terrain is sensed through visual touch, the gait is decided through a finite-state machine, the gait driving motion is executed, and through experimental verification, the self-adaptive gait can improve the moving efficiency and speed, and the amphibious environment adaptability of the robot is enhanced.

The invention relates to an improved riverbed erosion and deposition amount calculation method and system based on terrain classification encryption and a medium, and the method comprises the steps: 1, collecting the fixed section and underwater terrain observation data of a typical river reach; step 2, cutting an encrypted section based on topographic observation data; 3, calculating the erosion and deposition amount of each level of encrypted section; and 4, determining the erosion and deposition amount of the section of the river section and calculating the encryption series. According to the method, the terrain and section observation data are adopted, the river channel scouring and silting amount deviation calculated based on the terrain and the section is reduced, the section is subjected to multi-stage encryption through the river channel terrain observation data, the control index for judging that the encryption stage number is qualified is provided, and the calculation method for obtaining the riverbed scouring and silting amount based on the river channel storage change between the sections is further optimized; the riverbed scouring and silting adjustment degree of the riverway can be reflected more accurately and truly, and a foundation support is provided for river treatment.

The application provides a terrain recognition method, device and equipment for a legged robot and a medium. The method comprises the following steps: acquiring pose data collected by a body sensor of a target legged robot, and calculating state parameters of the target legged robot; inputting the pose data and the state parameters into a foot-ground contact force model to determine three-dimensional foot-ground contact force; generating a contact mask sequence representing a foot-end ground contact state of the target legged robot based on the three-dimensional foot-ground contact force, and segmenting the pose data to obtain a segmented pose data set; inputting the pose data set into a pre-trained terrain classification model to determine a confidence vector of the current ground under multiple terrain attributes, and determining a terrain category of the current ground according to multiple confidence vectors. Through the method and device, the autonomous adaptation capability and environmental perception reliability of the legged robot in complex terrain are significantly improved.

The invention discloses a power transmission line microtopography recognition method, system and device based on a multi-modal attention mechanism and a medium, and the method comprises the steps: obtaining power transmission line space corridor data and multi-source geographic information data of a target region, carrying out the preprocessing, and generating a line feature map and a multi-channel ground feature map; constructing a double-branch deep learning network model, inputting the line feature map into a topographic feature branch, inputting the multi-channel ground feature map into a line feature branch, and respectively extracting deep feature information; through a cross-branch space attention module, weighting the features extracted by the topographic feature branches by using a space attention weight generated by extracting the features by the line feature branches to obtain fusion features; and inputting the fused features into a decoder for up-sampling and feature reconstruction, and outputting a classification recognition result for the microtopography along the power transmission line corridor. According to the invention, full-process intelligent processing is realized, the identification efficiency is obviously improved, and the method is suitable for wide-range line census and risk assessment.

The invention discloses a terrain classification method and system based on a plate capacitance tomography sensor, and belongs to the technical field of robot application. The method comprises the following steps: firstly, designing a panel ECT sensor array comprising eight electrodes, and building a mobile robot body sensing data acquisition system, so as to acquire mutual capacitance data and inertial data of terrains; and secondly, performing feature extraction, fusion and classification on the capacitance data and the inertial data by building a multi-layer perceptron classification model, after the multi-layer perceptron model is trained, moving the robot to a known terrain, and completing the classification of the terrain through the multi-layer perceptron. According to the method, two kinds of modal information of inertia and capacitance are utilized, through combination of interactive vibration information of the robot and the terrains and changes of dielectric constants in the terrains, the terrains made of various different materials can be automatically recognized, and the method has high universality and practical significance.

A system and method for terrain classification is provided herein. The system includes a lidar that emits a laser pulse onto an environment and receives a reflected laser pulse off of a terrain of the environment, an analogue to digital converter that converts the reflected laser pulse into an input waveform, and a computer system that receives the input waveform. The computer system includes a correlation engine that may use a neural network. The correlation engine correlates the input waveform. The computer system includes a terrain classifier that classifies the fitted waveform in real-time to classify a property of the terrain.

This application proposes a method, device, and electronic device for acquiring fire rescue tactics. The method includes: acquiring the fire line classification features and terrain classification features of any first grid cell in a set of grid cells corresponding to the fire line; acquiring the feature vector corresponding to any first grid cell based on the first feature vector corresponding to the fire line classification features, the second feature vector corresponding to the terrain classification features, and the third feature vector corresponding to the environmental features of any first grid cell; and analyzing the feature vector corresponding to any first grid cell using a target graph convolutional neural network model and a target temporal Transformer model to acquire the fire rescue tactics corresponding to any first grid cell. This solves the technical problem in the prior art that fire relief methods cannot adapt to changes, resulting in a mismatch between the fire relief methods and the current fire situation, and poor fire extinguishing effect.

The application discloses a passability prediction method for unstructured terrain environment, and is applied to the technical field of vehicle traffic capacity prediction. The application constructs a three-dimensional grid map based on a multi-source sensor, combines semantic terrain classification and elevation structure feature extraction, and forms a geometric and semantic joint representation of the environment. By comparing the control instruction with the motion response of the vehicle in the actual terrain, a traffic capacity coefficient is constructed as a supervision signal, and a conditional variational autoencoder is trained to model the traffic capacity coefficient distribution and its uncertainty under different terrain conditions. Further, intermediate layer features are extracted from the semantic recognition network to establish a category statistical model for identifying terrain categories that do not appear in the training set. Finally, the traffic capacity confidence and terrain reliability are fused to output a grid-level traffic probability, realizing the passability prediction in the unstructured environment.

The application provides a topographic classification method and system based on typhoon wind speed prediction, and the method comprises the following steps: obtaining topographic basic data of a classification area; constructing a topographic multi-classification set based on regional meteorological model wind speed and the topographic basic data; obtaining typhoon wind speed prediction data and performing risk assessment on the topographic multi-classification set to obtain a risk index grading classification result corresponding to the topography; and obtaining a microtopography distribution map of the classification area based on the risk index grading classification result. The topographic classification method based on typhoon wind speed prediction can realize the risk assessment of a future line under a current wind speed environment by adding the typhoon wind speed prediction data into the topographic classification process for dynamic topographic classification, and the line operation and scheduling efficiency is improved.

The application relates to a photovoltaic pile driver terrain identification method and system, a terminal and a storage medium, and relates to the technical field of intelligent equipment. The application comprises the following steps: analyzing camera pixel data, laser radar data, a camera internal parameter matrix, a camera radar external parameter matrix and a radar satellite external parameter matrix to determine pixel camera data, radar world data and camera world data; fusing the radar world data and the camera world data to determine simultaneous space fusion data and coordinate point gray values; analyzing the coordinate point gray values and the simultaneous space fusion data to determine spatial terrain features and pile driving terrain classification; and analyzing the radar world data, the camera world data, the spatial terrain features and the pile driving terrain classification to determine real-time terrain modeling. The application has the effects of improving the photovoltaic pile driving terrain identification accuracy and terrain updating efficiency.

The land classification method based on deep and shallow feature and multi-attention mechanism fusion, electronic equipment and storage medium belong to the technical field of remote sensing image processing and deep learning semantic segmentation. In order to solve the problem of accurate classification of similar categories and category boundaries in land use classification method, the present application comprises the following steps: constructing a multi-scale normalized spatial attention mechanism layer; constructing a multi-scale normalized direction position attention mechanism layer; constructing a multi-scale normalized channel attention mechanism layer; constructing a land use classification network model, including a normalized multi-attention feature extraction network and a multi-deep and shallow feature fusion dilated convolution network; collecting data in the city-rural area adaptive land surface cover dataset; performing data preprocessing to obtain land data set; inputting the land data set into the constructed land use classification network model; performing land classification training based on deep and shallow feature and multi-attention mechanism fusion; and obtaining the land classification model based on deep and shallow feature and multi-attention mechanism fusion. The present application is accurate in classification.

The invention relates to the technical field of railway line selection, and discloses a terrain-driven intelligent site selection and line trend search method for a long and large mountain railway tunnel. The search method integrates three parts of topographic data preprocessing, topographic classification and tunnel portal search and route trend search, and specifically comprises the following steps: converting three-dimensional topographic data into spatial waves, and processing the spatial waves through Fourier transform to obtain topographic relief features; a low-pass filter is designed based on the representative tunnel length for filtering preprocessing; carrying out block classification on a topographic region by utilizing the processed topographic data, and searching a feasible tunnel portal position; grouping the potential tunnel portal positions; the construction cost is used as a main consideration factor, a bidirectional A algorithm is used for connecting tunnel portals, a plurality of feasible schemes are generated, and an optimal path is obtained through comparison and selection; and generating a specific three-dimensional railway line scheme by using the optimal path. The searching method is high in searching efficiency and suitable for railway line design in complex mountainous areas.

The application discloses a remote sensing image-based paleontological fossil producing area identification method and system, and belongs to the field of paleontological fossil exploration, comprising the following steps: acquiring GF-2 multispectral images, digital elevation data and geological map data, pre-processing the GF-2 multispectral images to obtain a land classification map, performing Gram-Schmidt fusion processing on a panchromatic band and a multispectral band, performing vectorization processing on the geological map data and extracting Qiongzhusi group stratum boundaries, and obtaining a slope factor map based on the digital elevation data; superimposing the Qiongzhusi group stratum boundary vector data on the fused GF-2 multispectral images, and extracting an image candidate area of the Qiongzhusi group stratum through spatial analysis. The application realizes efficient and accurate fossil exploration through multi-source data fusion and intelligent analysis technology, and effectively solves the problem of fossil feature identification in mixed pixels by combining a dynamic spectral decoupling algorithm with a professional fossil spectral feature library and solving component abundance through constraint energy minimization.

A land use classification method based on remote sensing images, comprising the following steps: step 1: obtaining the original image of remote sensing images of urban and township land, marking the pictures after screening, and a total of four categories: cultivated land, orchard, forest and building; step 2: cutting and data enhancement are carried out on the marked pictures to make a data set; step 3: the training set in the prepared data set is put into a feature extraction network and a multi-scale fusion up-sampling structure for training to obtain a segmentation model; step 4: the remote sensing image to be classified is put into the trained segmentation model to obtain the result; the purpose of the present application is to solve the problems of low small target precision and poor edge segmentation effect when the land categories are many and the features are complex in the land classification method based on remote sensing images, and a method capable of realizing more accurate identification result of various land types in remote sensing images is proposed.

The present invention relates to a system, vehicle, model training method, model, program for processing image data IMG(1) to identify a traversable region TRR of an off-road terrain ORT. The traversable region TRR represents a region TRR of the off-road terrain ORT which is traversable by a vehicle. The system includes one or more processor collectively configured to receive image data representing an image IMG(1) of the off-road terrain (ORT). The image data is captured by at least one imaging sensor provided on the vehicle. The image data is processed using an image segmentation model to segment the image IMG(1) into a plurality of image segments, the plurality of image segments identifying one or more terrain feature TF(1), TF(2) present in the off-road terrain ORT. In dependence on an operational capability of the vehicle, each of the one or more identified terrain features TF(1), TF(2) is classified as either being traversable, TTR or non-traversable (Figure 4B not shown, UTR). The traversable region TRR of the off-road terrain ORT is determined by identifying a region of the image IMG(1) which excludes any terrain features TF(1), TF(2) classified as being non-traversable (UTR). The system outputs traversable terrain data, TTD representing the traversable region TRR of the off-road terrain ORT.

This invention discloses a spatial downscaling method for weather forecast models based on a terrain classification super-resolution model. The method first obtains nested data at each layer through dynamic downscaling of the weather forecast model, then trains a super-resolution model using data at different resolutions of the target area. Following the above process, super-resolution models are established for different terrains, and a terrain classification model is trained using terrain data. The super-resolution model and the terrain classification model are combined to obtain a fused prediction result. This fused model is the SRBTC model, which considers spatial correlation. The model outputs the similarity probability between the area to be predicted and various different terrains. The prediction results are weighted and summed to obtain the fused prediction result of the super-resolution model. During model training, a specified value scaling method is proposed to consider the differences between high- and low-resolution meteorological simulation data. When applying the model, a splitting and merging method is proposed to consider the differences between multi-scale simulation data, and the applicability of the model is specified.