The invention discloses an improved high-resolution remote sensing image classification method based on deep learning. On the basis of the deep learning theory, a seven-layer convolutional neural network is designed; a high-resolution remote sensing image sample is inputted into the network to carry out network training and last two full connection layers obtained by learning are outputted as twodifferent high-level features of the remote sensing image; dimension reduction is carried out by using a principal component analysis for the output of the fifth pooling layer of the network, whereinthe result after dimension reduction is used as a third high-level feature of the remote sensing image; the three kinds of high-level features are fused in series; and then an effective logistic-regression-based classifier is designed to classify the remote sensing image. According to the invention, feature extraction is carried out on the high-resolution remote sensing image based on the deep learning theory and the features obtained by learning have high expressive force and robustness. Besides, the extracted high-level features are fused and the fused feature is inputted into the logistic regression classifier, so that the good classification result is obtained.

Improved high-resolution remote sensing image classification method based on deep learning

The invention discloses a saliency object detection method based on Faster R-CNN. The method comprises the steps of first performing multi-scale segmentation on an image, then outlining possible saliency objects using the Faster R-CNN, establishing an object analogue map, thereafter distributing a foreground specific gravity to a superpixel via foreground connectivity, then obtaining round and smooth saliency maps in combination with specific gravities of a foreground and a background using a saliency optimization technology, and at last performing fusion using an MCA (Multi-layer Cellular Automata) to obtain a final saliency map. The segmentation is performed on an input image on three scales using a superpixel segmentation algorithm, and the superpixel segmentation algorithm is to aggregate adjacent and similar pixel points into different sizes of image areas according to low-level characteristics such as a color, a texture and a brightness, such that the complexity of saliency detection can be effectively reduced; and by taking different scales of segmented images as a layer of cells and performing fusion on the different scales of superpixel segmented images using the MCA, theconsistency of an image saliency detection result is guaranteed.

Saliency object detection method based on Faster R-CNN

The invention relates to an image processing method and a device, an electronic device and a storage medium. The method comprises the following steps: preprocessing pixel points of an image to be processed to obtain a first image; inputting a first image into a first network to obtain a pixel connection map; determining a target area in the first image according to the pixel connection map. According to an image processing method of an embodiment of the present disclosure, by preprocessing the pixel points, the processing efficiency is improved, the target area is determined by the pixel connection graph, and the demand for training samples is reduced. Moreover, the first probability value and the first connected value in the pixel connection graph and other parameters reduce the processing complexity, and improve the recognition accuracy of the target area.

Image processing method and device, electronic device, and storage medium

The invention discloses an image saliency detection method and device, a storage medium and a processor. The image saliency detection method comprises the steps of performing super-pixel segmentation on a depth map of a target image to obtain a plurality of super-pixels of the target image; performing clustering processing on each super-pixel to obtain a first number of categories of each super-pixel, wherein the clustering center of a category containing the largest number of pixels is the target clustering center of each super-pixel in the first number of categories; and carrying out a first preset algorithm on the target clustering center of each super-pixel to obtain a depth saliency map of the target image. The accuracy of saliency detection for an image is improved according to the invention.

Image saliency detection method and device, storage medium and processor

The invention discloses a multi-scale coupled convolutional network-based automatic abnormal-target detection method of a railway contact network. Multiple final candidate regions of an image are obtained; the final candidate regions and sampling results of double downsampling are input into a multi-scale coupled convolutional network for parameter training and feature extraction; the extracted features are input into an extreme-learning-machine (ELM) classifier to classify the extracted features; and positions of candidate frames are corrected through using a regression device according to obtained candidate frame positions, and thus positions of abnormal targets, namely the positions of the corrected candidate frames, in image information are obtained. The method is suitable for use in automatic abnormal-target detection of the railway contact network, can obtain an abnormality detection effect which is more accurate than human eye observation, and is high in detection precision andan automation degree.

Multi-scale coupled convolutional network-based automatic abnormal-target detection method of railway contact network

The invention provides a video saliency detection method based on candidate area merging. The method comprises the following steps: after a video is divided into video frames, each video frame is divided into super pixels, and the spatial saliency and time saliency of each super pixel are calculated; then, a spatial saliency graph is merged with the time saliency graph by a self-adaptive weight allocation strategy to obtain a preliminary time and spatial saliency graph; and a final time and spatial saliency graph is obtained through selection, merging and optimization of candidate areas. The video saliency detection method of the invention can effectively overcome the influences of background clutter, camera movement and saliency target movement inconsistency, and accurately mark the saliency areas of the video.

A video saliency detection method based on candidate area merging

The invention discloses a convolutional neural network feature extraction method based on principal component analysis The convolutional neural network feature extraction method based on principal component analysis includes the steps: selecting the convolutional neural network which has been trained on an Imagenet data set, taking the convolutional neural network as the feature extractor of an image, extracting the feature mapping graph from the output of each pooling layer of the convolutional neural network, taking all the extracted feature mapping graphs of each layer as the deep featuresof the image, utilizing principal component analysis to perform dimensionality reduction on the deep features, and utilizing bilinear interpolation to reset the final result feature mapping graph tothe original image size to obtain the efficient image deep features The deep features obtained through convolutional neural network feature extraction method based on principal component analysis have rich semantic information of the image, are low in the feature dimension and small in data size, and can be used for various tasks of identification and classification of images

Convolutional neural network feature extraction method based on principal component analysis

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A video saliency detection method based on candidate area merging

Convolutional neural network feature extraction method based on principal component analysis