Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network

Abstract

Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image.

A brief analysis of remote sensing image cloud extraction method based on deep learning

“…If you want to improve the prediction accuracy of the model output results, you usually use the Full Connected Conditional Random Field (Full Connected CRF) model to smooth the rough prediction result images and edges. In the cloud detection process, thin clouds under different underlying surface types are also fuzzy, and the detection results of cloud system edge information under snow covered underlying surface types with fuzzy boundaries are also different, Therefore, in cloud detection, full connection conditional random field model will also be used to process cloud system edge details [60] Conditional random field (CRF) is a conditional probability distribution model for a given set of input random variables and another set of output random variables. Taking the conditional random field as the post-processing of model output data, it can not only take into account the spatial context information, but also reflect the interdependence between observation variables, refine and smooth the edges of model segmentation, and remove small error segmentation regions.…”