Abstract: A few-shot urban remote sensing image information extraction method based on meta learning and attention includes building a few-shot urban remote sensing information pre-trained model. During a pre-training stage, pre-training network learning is performed for a few-shot set to fully learn feature information of existing samples and obtain initial feature parameters and a deep convolutional network backbone of the few-shot set; the few-shot urban remote sensing information pre-trained model is a network structure including a convolutional layer, a pooling layer and a fully-connected layer, and includes five sections of convolutional network where each section includes two or three convolutional layers, and an end of each section is connected to one maximum pooling layer to reduce a size of a picture; the number of convolutional kernels inside each section is same, and when closer to the fully-connected layer, the number of convolutional kernels is larger.

Abstract: The disclosure provides a pedestrian re-identification method based on a spatio-temporal joint model of a residual attention mechanism and a device thereof. The method includes: performing feature extraction for an input pedestrian with a pre-trained ResNet-50 model; constructing a residual attention mechanism network including a residual attention mechanism module, a feature sampling layer, a global average pooling layer and a local feature connection layer; calculating a feature distance by using a cosine distance and denoting the feature distance as a visual probability according to the trained residual attention mechanism network; performing modeling for a spatio-temporal probability according to camera ID and frame number information in a pedestrian tag of a training sample, and performing Laplace smoothing for a probability model; and calculating a final spatio-temporal joint probability by using the visual probability and the spatio-temporal probability to obtain a pedestrian re-identification result.

Abstract: A method of optical and microwave synergistic retrieval of aboveground biomass, the method including: 1) obtaining an observation value of aboveground biomass (AGB) of a sample plot; 2) pre-processing laser radar (LiDAR) data, optical remote sensing data and microwave remote sensing data covering a research region, to yield crown height model (CHM) data, surface reflectance data and a backscattering coefficient, respectively; 3) extracting different LiDAR variables, extracting a plurality of optical characteristic vegetation indexes, and extracting a plurality of microwave characteristic variables; 4) establishing a multiple stepwise linear regression model of the biomass; 5) taking the biomass value of the LiDAR data coverage region as a training set and a verification sample set, and selecting samples for modeling and verification; 6) screening out the optical and microwave characteristic variables; and 7) constructing an optical model, a microwave model, and an optical and microwave synergistic model of AG

Abstract: The disclosure provides a pedestrian re-identification method based on a spatio-temporal joint model of a residual attention mechanism and a device thereof. The method includes: performing feature extraction for an input pedestrian with a pre-trained ResNet-50 model; constructing a residual attention mechanism network including a residual attention mechanism module, a feature sampling layer, a global average pooling layer and a local feature connection layer; calculating a feature distance by using a cosine distance and denoting the feature distance as a visual probability according to the trained residual attention mechanism network; performing modeling for a spatio-temporal probability according to camera ID and frame number information in a pedestrian tag of a training sample, and performing Laplace smoothing for a probability model; and calculating a final spatio-temporal joint probability by using the visual probability and the spatio-temporal probability to obtain a pedestrian re-identification result.

Abstract: A method of extraction of an impervious surface of a remote sensing image. The method includes: 1) obtaining a remote sensing image of a target region, performing normalization for image data, and dividing the normalized target region image into a sample image and a test image; 2) extracting an image feature of each sample image by constructing a deep convolutional network for feature extraction of the remote sensing image; 3) performing pixel-by-pixel category prediction for each sample image respectively; 4) constructing a loss function by using an error between a prediction value and a true value of the sample image and performing update training for network parameters of the deep convolutional network and network parameters relating to the category prediction; and 5) extracting an image feature from the test image through the deep convolutional network based on the training result obtained in 4).

Abstract: A method of optical and microwave synergistic retrieval of aboveground biomass, the method including: 1) obtaining an observation value of aboveground biomass (AGB) of a sample plot; 2) pre-processing laser radar (LiDAR) data, optical remote sensing data and microwave remote sensing data covering a research region, to yield crown height model (CHM) data, surface reflectance data and a backscattering coefficient, respectively; 3) extracting different LiDAR variables, extracting a plurality of optical characteristic vegetation indexes, and extracting a plurality of microwave characteristic variables; 4) establishing a multiple stepwise linear regression model of the biomass; 5) taking the biomass value of the LiDAR data coverage region as a training set and a verification sample set, and selecting samples for modeling and verification; 6) screening out the optical and microwave characteristic variables; and 7) constructing an optical model, a microwave model, and an optical and microwave synergistic model of AG

Abstract: A method of extraction of an impervious surface of a remote sensing image. The method includes: 1) obtaining a remote sensing image of a target region, performing normalization for image data, and dividing the normalized target region image into a sample image and a test image; 2) extracting an image feature of each sample image by constructing a deep convolutional network for feature extraction of the remote sensing image; 3) performing pixel-by-pixel category prediction for each sample image respectively; 4) constructing a loss function by using an error between a prediction value and a true value of the sample image and performing update training for network parameters of the deep convolutional network and network parameters relating to the category prediction; and 5) extracting an image feature from the test image through the deep convolutional network based on the training result obtained in 4).

Abstract: A method for reconstructing a super-resolution image, including: 1) reducing the resolution of an original high-resolution image to obtain an equal low-resolution image, respectively expressed as matrix forms yh and yl; 2) respectively conducting dictionary training on yl and yhl to obtain a low-resolution image dictionary Dl; 3) dividing the sparse representation coefficients ?l and ?hl into training sample coefficients ?l_train and ?hl_train and test sample coefficients ?l_test and ?hl_test; 4) constructing an L-layer deep learning network using a root-mean-square error as a cost function; 5) iteratively optimizing network parameters so as to minimize the cost function by using the low-resolution image sparse coefficient ?l_train as the input of the deep learning network; 6) inputting the low-resolution image sparse coefficient ?l_test as the test portion into the trained deep learning network in 5), outputting to obtain a predicted difference image sparse coefficient {circumflex over (?)}hl_test, computing

Abstract: A method for reconstructing a super-resolution image, including: 1) reducing the resolution of an original high-resolution image to obtain an equal low-resolution image, respectively expressed as matrix forms yh and yl; 2) respectively conducting dictionary training on yl and yhl to obtain a low-resolution image dictionary Dl; 3) dividing the sparse representation coefficients ?l and ?hl into training sample coefficients ?l_train and ?hl_train and test sample coefficients ?l_test and ?hl_test; 4) constructing an L-layer deep learning network using a root-mean-square error as a cost function; 5) iteratively optimizing network parameters so as to minimize the cost function by using the low-resolution image sparse coefficient ?l_train as the input of the deep learning network; 6) inputting the low-resolution image sparse coefficient ?l_testas the test portion into the trained deep learning network in 5), outputting to obtain a predicted difference image sparse coefficient {circumflex over (?)}hl_test, computing