81results about How to "Reduce the number of parameters" patented technology

The invention discloses a multi-scale image semantic segmentation method. The method comprises the following steps: obtaining a to-be-segmented image and a corresponding label; constructing a full convolutional deep neural network, wherein the full convolutional deep neural network comprises a convolution module, a hole convolution module, a pyramid pooling module, a 1 * 1 * depth convolution layer and a deconvolution structure; setting hole convolution as channel-by-channel operation, and utilizing low-scale, medium-scale and high-scale characteristics in a targeted mode; training the full convolutional deep neural network, establishing a loss function, and determining parameters of the full convolutional deep neural network by training the sample image; and inputting the to-be-segmentedimage into the trained full convolutional deep neural network to obtain a semantic segmentation result. By means of the method, the image semantic segmentation problem with complex details, holes andlarge targets can be well solved while the calculated amount and the parameter number are reduced, and the consistency of category labels can be reserved while the target edges can be well segmented.

The invention discloses a remote sensing image ground object classification method based on superpixel coding and convolution neural network, using adaptive superpixel coding and double channel convolution neural network. The remote sensing image ground object classification method based on superpixel coding and convolution neural network includes the steps: utilizing a superpixel algorithm to perform image pre-segmentation; using a cluster method to merge neighboring and similar superpixel blocks, setting the size of the taken blocks, constructing three double channel convolution neural networks with different input size; inputting samples with different taken block size into the corresponding network; using the convolution neural networks to extract the data characteristics of two sensors respectively; merging the extracted characteristics for classification; and according to the size of the merged pixel block, determining the size of the taken blocks of the samples, and realizing adaptive selection of the utilized neighborhood information. The remote sensing image ground object classification method based on superpixel coding and convolution neural network can realize adaptive selection of the utilized neighborhood information to enable the neighborhood information to realize positive feedback effect and preferably utilize the neighborhood information to send the samples to different networks according to the neighborhood information so as to enable the samples with similar distribution to enter the same network, thus effectively improving the classification accuracy.

The invention provides a GNSS multimode single-frequency RTK cycle slip detection method and apparatus. The method comprises the following steps: communicating with a GPS satellite, a GLONASS satellite, a Galileo satellite and a Beidou satellite and obtaining corresponding data; according to a formula, carrying out calculation so as to obtain a residual error vector of a station epoch secondary difference carrier wave observation value of each satellite; according to the residual error vector, calculating an RMS value, and if the RMS value is greater than or equal to a threshold EPS, determining that a satellite generates a cycle slip; according to a formula (2), calculating a standard residual error V<->; comparing |error V<->| with u<alpha/2>, and carrying out corresponding operation; and fusing the determined cycle slip with a cycle slip detected by use of a Doppler integration method. The invention brings forward a novel method for detecting a cycle slip by combining a multimode station epoch secondary difference method and a Doppler integration method. The multimode station epoch secondary difference method detecting the cycle slip based on a residual error domain only sets one station epoch relative clock error parameter, such that even if a single system has only one satellite, effective utilization and cycle slip detection can still be realized.