Super-resolution Reconstruction And Object Detection For Remote Sensing Images

Sparse representation has been widely used in the field of remote sensing image super-resolution(SR)reconstruction to restore a high-quality image from a low-resolution(LR)image.Owing to the lack of an inner relationship between image patches and an image's global information,the traditional methods of jointly training two over-complete dictionaries cannot obtain good SR reconstructed results.Therefore,we propose an effective approach for remote sensing image SR reconstruction based on sparse representation.First,we train two dictionaries for detail image patches and HR patches.Second,in order to enhance the inner relationship between image patches,we introduce a global self-compatibility model for global regularization.Finally,the sparse representation and local and nonlocal constraints are integrated to improve the performance of the model,and the fast adaptive shrinkage-thresholding algorithm(FASTA)is employed to solve the convex optimization problem in the GJDM.Compared to other methods,the results of the proposed method show good SR reconstruction performance in preserving details and texture information and significant improvement in peak signal to noise ratio(PSNR).With the significant improvement of remote sensing image resolution,the object detection technology has been developed.In many applications,we usually need more accurate object location information,but some object detection algorithms that only give the object category and rough location don't meet this requirement.Therefore,on the basis of Faster R-CNN,we extract image features,region proposals by deep convolutional network,use classifier to achieve object classification,and get the object approximate position by the regression.At the same time,combining the mature image segmentation technology,we integrate the object detection and convex image segmentation technology.Then we propose the exquisite object detection algorithm of remote sensing images,and achieve the object classification,detection and segmentation integrally.Compared with other object detection algorithm,this method not only can achieve the accurate object detection,but also can extract the shape and profile characteristics of object,make the precise positioning and detection achieve a better level.