319 results about "Land cover" patented technology

The invention discloses a remote sensing image multi-scale semantic segmentation method based on a coding and decoding network, and relates to the technical field of computer vision and remote sensing image processing, and the method comprises the following steps: S1, obtaining image data, carrying out the preprocessing of an image, carrying out the normalization of the image to a specified size, and carrying out the data enhancement operation, the data enhancement operation comprises random rotation, overturning and zooming. According to the remote sensing image multi-scale semantic segmentation method based on the coding and decoding network, ResNeXt5032x4d is introduced to serve as a backbone network, grouping convolution is combined, multi-scale features are effectively extracted, the accurate recognition capacity of the model for the land cover type is improved, and through the fusion strategy of the self-adaptive feature cooperation module AFCM, the remote sensing image multi-scale semantic segmentation method based on the coding and decoding network is obtained. According to the method, local and global context information is fused, the segmentation capability of the model on a large target is enhanced through a parallel multi-scale convolution layer and cavity convolution, the accuracy and integrity of a segmentation result are ensured, and a segmented region is smoother and more complete.

The invention relates to the technical field of remote sensing recognition, in particular to a remote sensing recognition method and system applied to ecological system investigation. The method comprises the following steps: acquiring multi-temporal remote sensing image data of a target area; extracting a land cover type of the multi-temporal remote sensing image data, and performing dominant human activity area identification on the multi-temporal remote sensing image data according to the land cover type to generate dominant human activity area data; acquiring night light data of the target area; performing space-time registration on the night light data of the target area and the multi-temporal remote sensing image data to generate fused night light remote sensing data; performing boundary region extraction on the multi-temporal remote sensing image data through the land cover type to obtain edge region data; and calculating a vegetation index and a noctilucence index of the marginal region data based on the fused noctilucence remote sensing data. According to the method, through multi-temporal and multi-source data fusion and multi-index time sequence analysis, the accuracy and comprehensiveness of ecological system investigation remote sensing recognition are improved.

The invention discloses a high-precision topographic mapping system and method based on multi-source remote sensing data, and relates to the technical field of topographic mapping, and the method comprises the steps: obtaining an optical remote sensing image, a radar remote sensing image, LiDAR point cloud data and meteorological index data of a target region, and carrying out the data preprocessing of the optical remote sensing image, the radar remote sensing image and the LiDAR point cloud data; obtaining the surface feature types of the target area and the coverage area of each surface feature type, and carrying out the weighted fusion of the optical remote sensing image and the radar remote sensing image of the target area according to the surface feature types of the target area and the coverage area of each surface feature type, and obtaining a self-adaptive fusion remote sensing image; according to the method, points in LiDAR point cloud data are divided into distinguished ground points and non-ground points, a digital elevation model is constructed according to the ground points in the LiDAR point cloud data, three-dimensional modeling of the terrain is performed on the basis of the digital elevation model in combination with texture information of a self-adaptive fusion remote sensing image, a three-dimensional terrain model is generated, and the terrain surveying and mapping precision is remarkably improved.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

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 relates to the technical field of ecological environment monitoring and geographic information, in particular to a coastal wetland soil sample point quality evaluation method and system based on multi-source environmental data. According to the method, remote sensing, climate, terrain, ocean and historical land coverage data are fused, and indexes are extracted and standardized; an environment similarity score (weighted Markov / neighbor weighted Euclidean), a historical consistency score (multi-temporal coverage + transfer penalty) and a source reliability score (metadata integrity + anomaly detection + NDVI-SOC residual) are constructed respectively; comprehensive scores are obtained through self-adaptive weight fusion, A / B / C grades are divided, and a report and a high-quality sample point set are output. The system comprises a data access module, a scoring engine module and a visualization module, and the precision and robustness of soil mapping and carbon storage evaluation can be remarkably improved.

The invention discloses a remote sensing image cloud removal method and system based on geographic coordinate embedding, and the system employs a geographic coordinate system embedding module to encode pixel longitude and latitude information into high-dimensional features, and achieves the joint modeling of an absolute position and a relative space relation through spherical coordinate mapping and a rotation matrix. And in combination with a multi-head self-attention mechanism and residual connection, the position sensitivity of the feature space is enhanced. Meanwhile, by using a multi-level feature extraction network and a cloud distortion compensation module, through a residual block and convolution operation, cloud interference features are extracted, a position sensing correction vector is generated, nonlinear offset of a cloud coverage area is corrected, and earth surface textures are recovered. According to the method, the adaptability of a cross-regional scene is remarkably improved, texture distortion and information loss of a large-scale cloud region are effectively reduced, the method has the advantages of high precision and high robustness in the fields of remote sensing image preprocessing, agricultural monitoring, land cover change analysis and the like, and a new scheme is provided for high-quality application of optical remote sensing data.

The invention discloses a multi-temporal remote sensing image automatic change detection method based on deep learning, and the method comprises the following steps: collecting remote sensing image data obtained in different periods in a target region, carrying out the standardization processing of the remote sensing image data, and obtaining a standardized multi-temporal remote sensing image data set; based on the standardized multi-temporal remote sensing image data set, carrying out ground feature category segmentation by adopting a semantic segmentation network to obtain a ground feature semantic priori graph; inputting the standardized multi-temporal remote sensing image data set into the improved U-shaped twin network, and outputting a change detection probability graph and a binary change mask; based on the change detection probability graph and the binary change mask, attribute discrimination is carried out on the change area in combination with the ground feature semantic prior graph, and a change area result is output; and integrating the change area result to a geographic information service platform. The improved U-shaped twin network is adopted, and automatic change detection of the multi-temporal remote sensing image is achieved.

The disclosure provides an improved canopy height correction method. The method includes: obtaining GEDI LiDAR data, airborne canopy height data, GDEM with high resolution and land cover product within the selected target area and timeframe; performing quality filtering and spatial-scale filtering on GEDI footprints; extracting laser pointing parameters and waveform parameters; extracting reference canopy height from airborne data for each footprint; extracting laser pointing parameters and waveform parameters; preprocessing the GDEM and calculating topographic parameters, including topographic variability index (TVI); constructing the Laser Pointing and Topographic Index (LPTI) according to the 3D forest-ground geometry model; inputting the waveform parameters, even topographic parameters, TVI and LPTI as independent variables, and the reference canopy height as the dependent variable to modeling an improved forest canopy height extraction, and utilizing the improved canopy height extraction model to correct the twofold influence of topographic on GEDI canopy height extraction.

The invention discloses a large-scale forest land carbon reserve estimation method and system based on an active and passive remote sensing technology. The method comprises the following steps: acquiring satellite-borne laser radar data of different sensors in a research area, SAR data of an L wave band and a C wave band, multispectral remote sensing image data, airborne laser radar data, topographic data and land coverage category data containing forest land; discontinuous combined spaceborne laser radar canopy height products are obtained by using spaceborne laser radar data of different sensors; improving a geographic weighted regression model to estimate the canopy height of a continuous scale; extracting polarization parameters by using C-band SAR data, extracting vegetation indexes and texture indexes by using multispectral remote sensing image data, constructing characteristic variables together with topographic data, and screening the characteristic variables based on a variance reduction criterion; and constructing an overground carbon reserve inversion model, and carrying out large-scale forest land carbon reserve estimation. According to the method, the forest land carbon reserve estimation precision of the large-scale regional broken plot is improved.

The invention discloses a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

The invention discloses a regional thermal environment analysis implementation method and device and a storage medium, and relates to the technical field of thermal environment analysis. According to the regional thermal environment analysis implementation method, the change trends corresponding to all adjacent positions of different land cover types in remote sensing data can be mined by lengthening the time dimension, and the weight parameter corresponding to each land cover is determined by combining all the change trends; the weight parameter can measure the influence degree of the earth surface coverage on the thermal environment of the target area. And finally, analyzing the thermal environment corresponding to the target area according to all the land surface coverage and the weight parameter corresponding to each land surface coverage. Therefore, the effective content of the remote sensing data can be fully mined, so that the accuracy and reliability of regional thermal environment analysis are improved.

The invention discloses a drought disaster risk quantitative assessment method fusing an irrigation disaster reduction effect, and belongs to the field of disaster risk assessment, and the method comprises the steps: obtaining different meteorological data of a target region in a three-dimensional space grid scale, and calculating a drought index; a drought event is identified by using an ST-KMeans clustering method; drought events are screened and combined, the space overlapping degree of the clustered drought region and the historical disaster region is calculated, and risk loss data docking is carried out; generating a drought event characteristic dynamic data set, and measuring the drought event; and in combination with the land coverage and utilization data of the target area and the farmland irrigation area data, extracting irrigation response information serving as a drought adaptability measure, and carrying out irrigation disaster reduction capability quantification and risk assessment. According to the method, irrigation information can be integrated on the basis of drought event identification, the drought slow release effect of irrigation in different time and space scales is quantitatively evaluated, and a scientific basis is provided for drought disaster risk management and agricultural adaptability policies.

The invention relates to a remote sensing image semantic change detection method based on difference feature guidance, and the method comprises the steps: obtaining difference features through employing a pixel-level subtraction method, employing a guidance segmentation branch to only focus on the segmentation of a change region, finally generating an accurate land coverage map, and guiding a change branch to generate a clear edge of the change region. According to the method, semantic segmentation is more accurate, and the edge of a change region is clearer.

The invention belongs to the technical field of soil improvement, and particularly provides a photovoltaic power generation field soil improvement method suitable for a high and cold gobi area. According to the method, the base material is laid on the pretreated soil firstly, then corresponding plant combinations are selected for mixed sowing according to habitat types of different areas, and finally regular irrigation and maintenance are performed, so that the water and fertilizer retention performance of the soil of the photovoltaic power generation field in the alpine and dry gobi desert area of the Tibet Plateau can be effectively improved; through comprehensive application of biological blankets, organic matter addition, microbial agent regulation and land covering technologies, the water and fertilizer retention performance of soil is improved, soil improvement and ecological restoration are promoted, and collaborative development of a power generation system and the ecological environment is achieved.

The invention discloses a land coverage classification method based on inference segmentation, and relates to the technical field of remote sensing image inference segmentation and deep learning. Comprising the steps of training sample set establishment, multi-scale feature extraction submodule design, cross-modal feature fusion module design, land coverage classification model structure design based on reasoning segmentation, land coverage classification model training based on reasoning segmentation, model performance evaluation and index analysis. According to the method, effective combination of remote sensing image features and semantic information is realized, and the differentiated cognitive ability for different ground object targets is improved. Meanwhile, a remote sensing image land cover intelligent classification technology with practical value is obtained, the professional technical threshold is remarkably reduced, the intelligence and universality of remote sensing image interpretation are realized, and a convenient remote sensing information acquisition method is provided for users in various fields.

The invention relates to an edge-guided cross-modal interactive fusion land cover classification method, which is particularly suitable for collaborative semantic segmentation of an optical image and a synthetic aperture radar (SAR) image. The method comprises the following steps: constructing a pseudo twin multi-stream encoder to respectively extract multi-scale features of optical and SAR images, embedding a cross-modal adaptive feature interaction module between encoding stages, realizing dynamic alignment and re-calibration of features between modals through a channel-level and space-level interaction mechanism, and relieving fusion deviation caused by heterogeneity and spatial dislocation. Neuron-level attention and multi-scale depth separable convolution are further fused through a lightweight feature fusion module, noise is suppressed, and context information is aggregated. And meanwhile, an edge auxiliary module is introduced to extract multi-scale boundary information from low-level features, and the multi-scale boundary information and semantic features are jointly decoded, so that the boundary detail and small target recognition capability is improved. According to the method, the precision and robustness of land coverage segmentation in a complex scene are remarkably improved on the premise of ensuring the calculation efficiency.

The invention relates to the technical field of geographic information measurement, in particular to a landform surveying method and system for territorial space planning, and the method comprises the steps: 1, carrying out the fusion preprocessing of multi-source data; 2, constructing a planning constraint dynamic rule base; 3, space-time constraint coupling analysis: taking the digital elevation model as a reference, and correcting the space offset of the land cover map through a thin-plate spline function; comparing the planning threshold value with the current landform parameter in real time, and marking a conflict coordinate set of gradient overrun or settlement overspeed; 4, intelligently segmenting the landform units; and 5, planning suitability output: setting a dynamic buffer distance according to the fault activity grade and the rock-soil body type, and generating an early warning layer when a town unit exists in a buffer area. According to the method, remote sensing images, laser radar point clouds and geological survey data are fused, space-time dislocation among various data sources is eliminated, and the precision of space analysis is improved.

The invention provides a coastal zone land coverage type classification method based on a random forest and a topology rule, and belongs to the technical field of coastal zone land coverage type classification. The method comprises the following steps: based on a GEE cloud platform, integrating a multispectral remote sensing image and elevation data, extracting spectral features and geographic features, and realizing pixel-level classification of random forest machine learning; generating a clustering unit through a simple non-iterative clustering method, and performing object-oriented special classification on the water body and the aquaculture pond in combination with spectral and morphological characteristics of the clustering unit; spatial filtering is introduced to remove salt and pepper noise, a coastal zone ground feature topology rule base is established to carry out spatial logic verification, and coastal zone land coverage classification with high precision and reasonable spatial layout is realized. According to the method, the multi-source remote sensing data and the multi-level classification strategy are fused, the problems of coastal zone ground feature spectrum confusion, boundary fuzziness and space logic inconformity are effectively solved, the classification precision and practicability are remarkably improved, and the method is suitable for land coverage monitoring and management of the complex coastal zone environment.

The invention discloses a farmland protection forest area windproof effect evaluation method based on a land-gas coupling model, and relates to the technical field of agricultural science, high-precision topographic data of a target farmland protection forest area is collected and preprocessed, and spatial distribution information of a farmland protection forest is obtained by combining remote sensing image interpretation; on the basis of topographic data and spatial distribution information, topographic relief and earth surface coverage features are analyzed, and a refined underlying surface classification system is constructed; and configuring high-resolution grid parameters of a WRF mode, and coupling a Noah-MP land surface process model. According to the farmland protection forest area wind-proof effect evaluation method based on the land-gas coupling model, through combination of high-resolution topographic data and a multi-source remote sensing image, topographic relief and surface heterogeneity of a farmland protection forest area can be accurately depicted, and a WRF mode is coupled with a Noah-MP land surface process model; fine simulation of complex terrains and various underlying surfaces is realized, the limitation of traditional uniform underlying surface hypothesis is overcome, and the accuracy of windproof effect evaluation is remarkably improved.

The invention belongs to the technical field of remote sensing image processing, computer vision and deep learning, and particularly relates to a remote sensing image erosion gully semantic segmentation method based on improved OfficientNet-UNet. Comprising the following steps of 1, data preparation and data preprocessing; 2, constructing and enhancing a data set; step 3, model construction and strategy training; and 4, performing contrast experiment and result evaluation. According to the method, detail features of ground features can be more accurately captured, an overfitting phenomenon caused by too high model complexity is reduced, so that the classification precision and boundary recognition accuracy of land coverage data are effectively improved, weights of different types of samples can be automatically adjusted in the training process, and the training efficiency is improved. Particularly, the contribution of background pixels to a loss function is reduced, so that the problem of dominant training of the background pixels is effectively relieved; the method has good expansibility.

The invention discloses a land cover classification model for multi-scale remote sensing images. The land cover classification model comprises a ViT encoder, a low-rank fine tuning module, a CNN encoder, a decoder, a scale encoder and a position encoder. A multi-resolution remote sensing image is processed by a ViT encoder block coding layer and then enters a Transform layer, interactive fusion is carried out on the multi-resolution remote sensing image and CNN features processed by a CNN encoder, fused visual features, scale codes and position codes are fused through an attention mechanism and then enter a decoder, the decoder obtains the category probability corresponding to each pixel through multi-step operation, and the category probability corresponding to each pixel is calculated through the corresponding category probability. And finally, determining a ground feature category through the semantic segmentation head. According to the method, single-model cross-resolution automatic interpretation is realized, and multi-element ground feature classification of remote sensing images with the resolution of 2-10m is supported. A visual pre-training large model is introduced to improve the interpretation stability; global context and local detail information are considered through ViT and CNN double branches; and semantic alignment up-sampling is introduced in the decoding process, so that the segmentation accuracy is improved.

The invention provides a global biodiversity hot spot area threat condition remote sensing monitoring method and system, and relates to the technical field of ecological environment monitoring, spatial analysis and remote sensing and information processing, and the method comprises the steps: obtaining multi-temporal global land coverage data and auxiliary GIS data of a biodiversity hot spot area, and carrying out the preprocessing; extracting forest coverage layers of a reference period and a monitoring period from global land coverage data, and extracting pixels converted from a forest to a non-forest through a layer difference method to generate a forest loss layer; based on the forest loss map layer and the preprocessed auxiliary GIS data, calculating various ecological pressure indexes including a forest loss rate, a protection failure rate and a population pressure index, and performing normalization; and calculating a comprehensive threat index based on each ecological pressure index, and determining an ecological threat level. According to the method, automatic identification, quantitative evaluation and grading of the ecological threats of the hot spot region are realized, and technical support is provided for global ecological protection planning and management.

The invention discloses an earth surface short-wave radiation product production method and device and a long-short-term memory network training method and device, and relates to the technical field of aerospace, and a specific implementation scheme is as follows: extracting a first short-wave radiation data set and a second short-wave radiation data set which are aligned in time from first earth surface short-wave radiation data collected by a satellite; acquiring a photovoltaic short-wave radiation data set which is acquired by a ground photovoltaic radiometer and is aligned with the first short-wave radiation data set in time; determining an albedo data set based on the satellite albedo product and the surface land cover product; obtaining an input short-wave radiation data sequence based on the first short-wave radiation data set, the second short-wave radiation data set, the albedo data set, the photovoltaic short-wave radiation data set and the ground photovoltaic radiometer coordinate vector point; inputting the input short-wave radiation data sequence into a pre-trained long short-term memory network to obtain an initial earth surface short-wave radiation product; and obtaining a target earth surface short-wave radiation product based on the initial earth surface short-wave radiation product.

The invention relates to a remote sensing interpretation method and system integrating multi-source spatio-temporal spectrum characteristics and a visual model. The method comprises the following steps: firstly, acquiring and preprocessing an optical image, harmonic and synthetic aperture radar data of a target area; time spectrum features of the optical image and the harmonic data are extracted respectively, and fusion features are generated through cross attention mechanism fusion; carrying out image serialization, time coding and space coding processing on the fused features and synthetic aperture radar data, and extracting spatio-temporal features by using a self-attention mechanism; and finally, decoding the spatio-temporal features and fusing a multi-source feature convolution result to generate a pixel-level land coverage classification result. According to the method, harmonic data are introduced to capture the time change trend of the ground features, a visual Transform self-attention mechanism is utilized to capture the space-time interdependence relationship, multi-source data are combined to capture the multi-dimensional features of the ground features, the limitation of a single data source is avoided, the multi-source time sequence features are fused to enable the model to adapt to different geographical environments, and the generalization performance is improved.

The invention provides a method and system for determining appropriate granularity for landscape pattern analysis, and the method comprises the steps: enabling high-resolution land cover or land utilization vector data of a target region to generate a first multi-granularity sequence with a coarse step length according to a maximum area value rule (Rule of Maximum Area, RMA), and determining a granularity upper threshold value and a scale domain according to three conservation criteria of landscape composition, area and pattern form; a second multi-granularity sequence in the scale domain is generated with a fine step length according to the RMA rule, and a landscape pattern index, sensitive to the scale effect, of the second multi-granularity sequence is calculated; the method comprises the following steps: performing mutation analysis by adopting a non-parametric rank test (Mann-Kendall and Pettitt test), and taking an effective mutation point with the highest frequency as an appropriate granularity; according to the method, information loss and nonparametric mutation inspection are quantified through three conservation criteria, the problems that a traditional method neglects landscape pattern form loss, granularity judgment is high in subjectivity, data distribution depends and the like are solved, and the objectivity of granularity decision and ecological application precision are improved.

The invention discloses a habitat quality influence assessment method based on an InVEST model. The habitat quality influence assessment method comprises the steps of S1, acquiring land cover classification data and threat factor data of an assessment area; s2, on the basis of the land cover classification data and the threat factor data, a habitat quality spatial distribution diagram of the evaluation area is calculated by using a habitat quality module in an InVEST model; s3, based on the habitat quality spatial distribution map, combining selected driving factor data, and utilizing a geographic detector model to quantitatively analyze the interpretation force of each driving factor on the habitat quality spatial diversity; s4, performing spatial overlay analysis based on the habitat quality spatial distribution map and ecological protection red line range data of the evaluation area; the method has the beneficial effects that the independent interpretation force of multiple driving factors such as natural geography and human activities on the habitat quality spatial diversity is quantitatively revealed by introducing the geography detector model, and the interaction effect between the factors can be further detected.

The invention discloses a personnel vulnerability probability assessment method considering landslide migration and personnel escape. The method comprises the following steps: investigating a living environment of residents around a landslide, and compiling land coverage types in the living environment into a geographic vector file; dividing a land coverage area for personnel escape in the geographic vector file into a plurality of sub-areas, and determining a personnel escape network; estimating available time for residents in each sub-region to escape; estimating the escape demand time of residents in each sub-region; and establishing a performance function representing successful or failed escape of the personnel, and respectively establishing personnel vulnerability probability evaluation formulas of an individual level and a group level according to the performance function representing successful or failed escape of the personnel. The method can be reproduced in other areas with landslide risks, so that an optimal escape route before a disaster is effectively planned, and important scientific basis and practical reference are provided for landslide disaster prevention and reduction work.

The invention discloses a space remote sensing image analysis method for detecting surface water and underground water exchange in a river region based on surface temperature, and belongs to the field of remote sensing detection surface temperature anomaly analysis. According to the method, background field construction is carried out on pixels in a space by combining single-scene high-resolution surface temperature data and land cover classification data, and temperature thermal anomaly of a river main stream is extracted by adopting an adjacent pixel temperature anomaly analysis method, namely, the temperature thermal anomaly of the river main stream is extracted by analyzing subtle change of surface temperature in a remote sensing image; and identifying abnormal pixels which are obviously different from the temperature characteristics of the surrounding area. The abnormal pixels often correspond to areas where surface water and underground water exchange is active, such as permeable zones, spring water exposure points and the like. The method can be applied to drainage basin water resource management and ecological protection, and the conversion path and intensity between surface water and underground water can be deduced by further analyzing and processing the abnormal pixels extracted by the method and combining background information such as geology and hydrology.

The invention discloses a land coverage classification method based on time series data, which comprises the following steps of: acquiring multispectral image data, cutting according to a geographic boundary of a research area, and extracting surface reflectance data of the research area through wave band superposition; by calculating a normalized difference vegetation index and a tasseled cap transformation humidity component, surface vegetation phenology and soil humidity are captured; an optimal time sequence feature set is screened based on time sequence difference evaluation indexes, and data redundancy is reduced; a land coverage classification result of the research area is generated through a support vector machine classifier in combination with the optimal time sequence feature set; and obtaining a verification sample in combination with the Google Earth high-resolution image, calculating overall classification accuracy (OA) and a Kappa coefficient according to the verification sample and a land cover classification result, and evaluating the reliability of the land cover classification result. Through combination of time sequence data and an intelligent classification strategy, the classification precision of urban complex ground features is significantly improved.