Research On Aerial Remote Sensing Photoelectric Image Preprocessing And Target Feature Extraction Technology

Remote sensing technology has developed at a high speed in recent years,and the resources of remote sensing photoelectric images are becoming a valuable asset.Nowadays researchers are focusing on making good deal with remote sensing images scientifically and efficiently,so that the advantage of multi-dimensional information can be fully taken.On the one hand,there still has great demand for remote sensing image preprocessing.The images collected by aircrafts at high altitude are often interfered by clouds and fog,which degrade the image quality and make the target unclear,affect the tasks like geographic mapping and reconnaissance.On the other hand,traditional image feature extraction method which based on prior knowledge is limited in most scenarios and can't meet the needs of tasks.Neural network in deep learning provides a new solution for solving problems above.This paper focuses on the remote sensing image's preprocessing and feature extraction,proposes innovative method based on the image dehazing and convolutional neural network:(1)At present,the model-based dehazing algorithm can remove the cloud based on the principle of atmospheric scattering,but it does not perform well in bright areas such as the sky,which easily leading to image distortion,and the contrast of the image is difficult to control.The non-model method's calculation amount is small,with the processing speed is faster than model-based.But the applicability of non-model method is insufficient in multiple different scenarios.In order to solve the problem,this paper proposes a improved image dehazing algorithm that make use of the image's pixel loss function.The pixel loss between hazed image and dehazed image is used to be an evaluation standard for dehazing quality.The algorithm combines the model-based method with the non-model method to take the advantages of image restoration and image enhancement.The experimental data of several algorithm can prove that the algorithm proposed has significance for image dehazing.(2)In the field of traditional image feature extraction,image target features such as texture,edge,and color features are usually extracted based on priori knowledge.These priori methods consume less computing resources and has been able to be used on some airborne platforms such as drone.As the demand of image processing grows and market expands,the traditional methods can't be satisfactory any more.Deep learning and neural network are becoming the mainstream in image feature extraction research.Based on the existing research of convolutional neural network,this paper proposes a lightweight remote sensing image feature extraction network,which combines the deep abstract features with the shallow figurative features,reduces the loss of the feature in network transmission process.At the same time,the algorithm extracts the multi-scale and multidirectional Gabor feature vector using the traditional priori-based method.Compared with the deep convolutional neural networks,the network which is integrated with Gabor feature can alleviate the computational burden.The algorithm is proved to perform better than single convolution neural network through the experiments which include large number of data sets.