16 results about "Optical radar" patented technology

The invention relates to the technical field of remote sensing image processing, in particular to a wetland information extraction method and system based on multi-source remote sensing data fusion, and the method comprises the following steps: obtaining and fusing a wetland optical radar image and water level time sequence data, extracting near-infrared and back-scattering multi-source characteristic parameters to discriminate water body pixels, and extracting a water body image; and carrying out statistics on spatial connectivity aggregation to generate a water body plaque boundary, calculating an area perimeter and shape index comprehensive model to classify wetland types, and adjusting mismatching labels in combination with vegetation coverage and soil moisture to form a wetland information extraction result map. According to the method, the water body differential expression is enhanced through combined reflection and scattering characteristics, the water body and peripheral differential expression is enhanced through combined reflection and scattering characterization, boundary transition aliasing is restrained through bidirectional threshold constraint, plaque space integrity is restored and maintained through connectivity analysis, and type classification sensitivity is enhanced through morphological parameter combination. And the wetland expression accuracy and the type stability are improved through ecological attribute consistency correction.

The invention belongs to the technical field of remote sensing image processing agricultural information, and particularly relates to a DS-xNet-based multi-source remote sensing time sequence data cultivated land utilization current situation extraction method. According to the method, the cultivated land and the water body can still be reliably recognized under the cloud or fog condition by combining Sentinel-1 and Sentinel-2, missing detection and misjudgment caused by cloud shielding are reduced, sNET captures short-term dynamic conditions, and mNET matrix memory enhances long-term dependence modeling, so that the recognition precision of seasonal and interannual changes is improved, supervision is applied to a branch and fusion layer, and the recognition accuracy of the cultivated land and the water body is improved. According to the method, each branch can learn independent discrimination capability and can cooperatively improve fusion output, over-fitting is reduced, generalization capability is improved, training convergence is accelerated, channel splicing retains discrimination information of each modal in a fusion stage, modal information conflict or loss caused by direct early fusion is avoided, classification accuracy is improved, and classification efficiency is improved. Various optical and radar indexes are combined and used to enhance the distinguishing capability of paddy fields, irrigation areas, different crops and non-cultivated areas.

The invention discloses a land type intelligent identification and updating method and system for land investigation, and relates to the technical field of land investigation and remote sensing land type identification, and the method comprises the steps: carrying out the unified coding and weight fusion of optical, radar, terrain and auxiliary features on a standardized land block image stack through a feature fusion and dimension reduction model; generating plot-level fusion feature representation; jointly inputting the land block level fusion feature representation and the pixel statistical features in the land block into a land class recognition model, outputting a land block level land class category and confidence through a double-layer fusion structure, and generating a multi-temporal land class recognition result; and calculating a change credibility score of the land parcel based on a multi-temporal land class identification result and a time sequence observation index, and determining whether the land parcel belongs to a stable state, a suspected change or a confirmed change by comparing an index trend, class stability and space consistency. According to the method, collaborative identification of macroscopic structure information and microcosmic detail information is realized, and the accuracy and the spatial integrity of regional land class classification are improved.

This invention relates to the field of optical imaging technology, specifically providing an optical radar co-aperture off-axis imaging system, comprising: an off-axis mirror assembly, a frequency-splitting device, a radar transceiver system, and an optical imaging system. The frequency-splitting device is disposed in the rear optical path of the secondary mirror and is used to reflect light waves and transmit microwaves. The frequency-splitting device includes a frequency-selective surface, which comprises an FSS pattern layer and a bias line layer. The FSS pattern layer includes periodically arranged FSS units. Nonlinear capacitors are electrically connected to the FSS units. The bias line layer applies a voltage to the nonlinear capacitors according to the received microwave band. By changing the voltage applied to the nonlinear capacitors, the resonant frequency of the FSS units is tuned, thereby changing the microwave passband frequency of the frequency-splitting device. This invention not only achieves a reflective wave-transmitting microwave design but also solves the problem that transmitting only a single band of microwaves in microwave-transmitting and light-reflecting methods is difficult to adapt to different task requirements.

This invention discloses a method, system, terminal device, and storage medium for inverting crop lodging parameters. The method includes: acquiring satellite optical radar remote sensing information and UAV optical radar remote sensing information; extracting features from the satellite optical radar remote sensing information and UAV optical radar remote sensing information to obtain remote sensing image feature information; fusing multi-source heterogeneous information from the remote sensing image feature information to construct a remote sensing image dataset; inputting the remote sensing image dataset into a deep neural network model for model training to obtain a crop lodging parameter inversion model; acquiring crop lodging image data; and inputting the crop lodging image data into the crop lodging parameter inversion model to obtain crop lodging parameters. This invention obtains rich crop lodging information by fusing satellite optical radar remote sensing information and UAV optical radar remote sensing information, and then inverts the lodging parameters through a parameter inversion model to obtain high-precision crop lodging parameters.

This invention relates to the field of remote sensing image processing technology, specifically to a method and system for wetland information extraction based on multi-source remote sensing data fusion. The method includes the following steps: acquiring and fusing wetland optical radar images and water level time-series data; extracting near-infrared and backscattering multi-source feature parameters to distinguish water body pixels; statistically aggregating spatial connectivity to generate water body patch boundaries; calculating a comprehensive model of area, perimeter, and shape index to classify wetland types; adjusting mismatched labels by combining vegetation cover and soil moisture; and forming a wetland information extraction result map. In this invention, the differences in water bodies are enhanced through joint reflection and scattering features; the differences between water bodies and their surroundings are enhanced through joint reflection and scattering characterization; boundary aliasing is suppressed through bidirectional threshold constraints; patch spatial integrity is restored through connectivity analysis; the sensitivity of type classification is enhanced through morphological parameter combinations; and the accuracy and type stability of wetland representation are improved through ecological attribute consistency correction.

The invention discloses a disease and insect pest detection system for agricultural planting, and relates to the technical field of image analysis, the disease and insect pest detection system comprises a scanning module, an information processing module, a leaf segmentation module and a disease and insect pest qualitative module, the information processing module carries out space-time synchronization on original data from the visible light camera, the infrared thermal camera and the optical radar and carries out preprocessing on the original data, and the leaf segmentation module is responsible for inputting the processed data into a proper segmentation model so as to realize accurate identification and segmentation of crops in a planting area. The plant disease and insect pest qualitative module is responsible for carrying out qualitative treatment on plant disease and insect pest conditions according to leaf segmentation results, the scanning module comprises a visible light camera, an infrared thermal camera and an optical radar, the visible light camera is used for capturing high-resolution images above legume crops in a planting area, and the plant disease and insect pest qualitative detection system has the advantage of omnibearing detection.

The utility model relates to the technical field of optical instruments, and particularly discloses a non-charged radar receiving device based on a laser reflection principle, which comprises a laser emission optical fiber, a reflection type optical thin film assembly and a laser receiving optical fiber. The laser emitting optical fiber and the laser receiving optical fiber are located on the two sides of the outer portion of the opening of the reflective optical thin film assembly respectively and are symmetrically distributed. According to the utility model, the optical fiber medium replaces an electronic loop, and a physical isolation type signal channel is established; optical modulation and spatial distribution optimization of laser signals are realized through the concave disc-shaped reflecting film; a symmetric optical fiber array is constructed to improve the optical signal capturing efficiency, and an all-optical radar receiving solution with interference resistance, electromagnetic pulse interference resistance and high fidelity is formed.

This application relates to a method and apparatus for optical-radar ISAR image conversion based on generative networks. The method includes: analyzing and modeling the physical characteristics of the target in the ISAR image, and using a deep learning network as the main technical means to incorporate the distribution characteristics of ISAR scattering points into the loss function of the network training, ultimately converting the optical image of the target into the corresponding ISAR image. The constraint that the scattering point position matrices extracted from the original ISAR image and the reconstructed ISAR image are equal is added to the loss function, enabling the deep learning network to generate more realistic ISAR images.

The invention provides a vegetation carbon income and expenditure evaluation method and system based on remote sensing image data, and belongs to the field of ecological environment monitoring. The method comprises the following steps: synchronously acquiring optical, radar and hyperspectral multi-source remote sensing images, and extracting characteristic parameters such as a vegetation index, a leaf area index, biomass and carbon reserve after preprocessing by radiation correction, geometric correction and atmospheric correction; and constructing a CNN-RNN hybrid model, fusing spatial-temporal characteristics to learn a carbon cycle rule, and outputting carbon absorption, carbon emission and net income and expenditure results. The system integrates a data acquisition module, a preprocessing module, a feature extraction module, a model operation module and a result output module, and full-process automation is achieved. The method breaks through the limitation that a traditional method is insufficient in space coverage and low in timeliness, improves the evaluation precision and efficiency, and provides powerful support for ecological protection and climate change research.

The application discloses a kind of air-to-air missile interception probability sensitivity analysis method based on agent model.Interception probability has important significance in missile composite guidance, determines the miss distance and hit probability of missile terminal guidance phase, to effectively improve the interception performance of missile during midcourse guidance and terminal guidance shift, it needs to carry out sensitivity analysis on each error that influences missile interception probability, through sensitivity analysis, the influence size of each error is quantified.However, the timeliness of traditional interception probability calculation method is poor, to solve this problem, the application establishes the support vector machine agent model of particle swarm optimization, to obtain the interception probability calculation model with higher timeliness and accuracy.Based on the agent model, the sensitivity coefficients of 18 kinds of errors are calculated by Sobol index method.The results show that radar angle measurement error and optical radar angle measurement error have the greatest influence on air-to-air missile interception probability.The method shortens the calculation time while ensuring the accuracy, and provides a basis for further sensitivity analysis.

The invention relates to the technical field of optical imaging, and particularly provides an optical radar common-aperture off-axis imaging system, which comprises an off-axis reflector group, a frequency division light splitting device, a radar transceiving system and an optical imaging system, and is characterized in that the frequency division light splitting device is arranged on a rear-end light path of a secondary mirror and is used for reflecting light waves and transmitting microwaves; the frequency division light splitting device comprises a frequency selection surface, the frequency selection surface comprises an FSS pattern layer and a bias line layer, the FSS pattern layer comprises periodically arranged FSS units, the FSS units are electrically connected with a nonlinear capacitor, the bias line layer is used for applying voltage to the nonlinear capacitor according to the wave band of received microwaves, and the nonlinear capacitor is connected with the frequency selection surface. The resonant frequency of the FSS unit is tuned by changing the voltage applied to the nonlinear capacitor, so that the microwave passband frequency of the frequency division light splitting device is changed. According to the invention, the reflection wave and microwave transmission design is realized, and the problem that only single-band microwaves are transmitted and different demand tasks are difficult to adapt in a microwave and reflection wave transmission mode is solved.

The utility model discloses a self-checking device of a flame detector, which comprises an infrared sensor, an ultraviolet sensor, an infrared light source and an ultraviolet light source which are connected with a microprocessor, and the infrared sensor and the ultraviolet sensor are respectively connected with the infrared light source and the ultraviolet light source for self-checking and are close to a glass window in front of the sensors; the infrared sensor and the circuit thereof are used for receiving optical radiation energy generated by flames and converting the optical radiation energy into voltage signals; the ultraviolet sensor and the circuit thereof are used for receiving optical radiation energy generated by flames, when the flames exist, the ultraviolet sensor is changed into a conducting state from a high-resistance state, and a microprocessor of the ultraviolet sensor is further connected with an optical radar circuit for shielding detection within an effective distance and a defrosting circuit. The self-checking device of the flame detector realizes self-checking of a light path of the sensor, improves the accuracy of flame detection, and reduces the false alarm rate.

The application discloses an optical radar, a movable device and an optical radar attachment detection method. The optical radar comprises a transceiver assembly, a window sheet, a detection module and a control module. The transceiver assembly is used for emitting an outgoing light beam and receiving a return light beam. The window sheet is installed on a corresponding light path of the transceiver assembly. Part of the outgoing light beam emitted by the transceiver assembly is reflected by the window sheet to form a reflected light beam, and the reflected light beam comprises a specular reflected light beam. The detection module is used for receiving the reflected light beam and generating a corresponding electrical signal. The control module is electrically connected with the transceiver assembly and the detection module. The control module is configured to determine whether an attachment is attached to the window sheet according to the corresponding electrical signal. The design can effectively detect whether an attachment is attached to the window sheet, thereby improving the detection accuracy of the optical radar.

The invention relates to the technical field of multi-source remote sensing image real-time seamless splicing, in particular to a multi-source remote sensing image real-time seamless splicing method based on an adaptive generative adversarial network, which comprises the following steps: a data preprocessing step: acquiring a multi-temporal or multi-source remote sensing image, performing radiation correction and geometric correction on the image, and obtaining a multi-temporal or multi-source remote sensing image; an adaptive generative adversarial network model construction step, a generator and a discriminator are constructed, the generator uses a convolutional neural network to receive the preprocessed image feature map and generate a spliced image, the adaptive generative adversarial network and a space-time module are introduced, model parameters are dynamically adjusted in the training and reasoning process, and the splicing image is obtained. To accommodate different sensor data (such as optical, radar or thermal infrared) and time series variations.

This invention relates to the field of optical radar technology, specifically providing an OPA control method based on phase switching time sorting. The method primarily involves reordering the scanning phases of the OPA within the measurement range, establishing a phase switching time function, arranging the corresponding groups of scanning phases and driving voltage values ​​to generate all possible arrangements, and then calculating to find the scanning phase arrangement that minimizes the phase switching time. This invention utilizes a sorting method to find the arrangement that minimizes the cumulative phase switching time among all achievable scanning angle arrangements. This minimizes the cumulative phase switching time of the overall OPA during scanning for different deflection phases, thus optimizing the upper limit of the system's operating speed.