Polarimetric SAR Image Information Representation And Classification Based On Deep Learning

Synthetic aperture radar(SAR)is an active microwave imaging system and it is one of the most important methods of Earth observation.SAR has the advantages of working under all weather conditions,large scope and certain penetration capacity,and it plays an important role in military and civilian fields.Modern SAR systems can provide polarimetric SAR(PolSAR)images by emitting and receiving fully polarized radar waves.The information representation of the PolSAR image has the characteristics of diversity and complexity.The extraction of scattering mechanism and polarimetric information requires further research.PolSAR image interpretation,such as visualization and classification,is a great challenge?Deep learning plays an important role in natural image processing and has shown excellent performance and great potential in PolSAR image interpretation.It provides new solutions for PolSAR image interpretation.Deep learning based PolSAR image interpretation methods still have some problems.First,PolSAR images have special imaging mechanism and the visual interpretation is difficult.The land cover information of PolSAR images is expressed by polarization-coherence matrix,and has various forms of scattering information representation.The visual appearance and data formation of PolSAR images are very different from optical images.Second,it is difficult to obtain a large number of accurately labeled samples for PolSAR image classification.Deep learning is difficult to learn the information representation of PolSAR images.The classification speed of a whole PolSAR image needs to be improved.Therefore,it is necessary to study deep learning models which have strong generalization ability and can learn the information representation of PolSAR images.With the help of the deep learning models,the PolSAR image interpretation performance can be improved.To solve the above problems,with the support of the sub-project of high resolution Earth observation system and the information fusion project,we conduct the researches on PolSAR image information representation and classification,and PolSAR image information visualization based on deep learning.Most deep learning based supervised PolSAR image classification methods can only classify a neighborhood window data to one class.The pixels in a neighborhood window may have different land cover types.The algorithm of outputting a 2-D classification map with Convolutional Neural Networks(CNN)is introduced to the PolSAR image classification.We use a Fixed-Feature-Size CNN(FFS-CNN),which can use the interrelation information representation of land cover types,to classify all pixels in a neighborhood window simultaneously.Experiments on a variety of PolSAR images,such as Gaofen-3,show that proposed method has faster classification speed,stronger generalization ability and higher classification accuracy.Furthermore,in order to make full use of hidden features of PolSAR data in rotation domain,Convolutional Long Short Term Memory networks(Conv LSTM)is used to learn the hidden information representation of the polarization coherent matrix sequence and the PolSAR image classification performance is further improved.It is difficult to obtain a large number of accurately labeled samples of PolSAR images,so unsupervised image classification is important for PolSAR image interpretation.Traditional machine learning based unsupervised classification methods have cumbersome pipelines.A method that uses the deep mutual information representation for unsupervised PolSAR image classification is proposed.The neighborhood window data of a pixel in a PolSAR image and its geometry transformed version are used to generate a paired sample as the input of a CNN.The deep features are used to compute deep mutual information representation,which can guide the CNN model to learn common information,and discard instance-specific details.Experiments on three real PolSAR datasets show that proposed method can achieve good unsupervised classification results and is superior to state-of-art methods.Proposed method is trained in an end-to-end manner and do not require extra preprocessing and post-processing.The deep features extracted by the CNN model also can be used to improve the performance of semi-supervise classification.Due to the imaging mechanism of SAR and the speckle noise,untrained people are difficult to recognize the land cover types visually from SAR images.A Supervised Cycle-Consistent Adversarial Network(S-Cycle GAN)is proposed to translate SAR images to the form of optical remote images and improve the SAR image visualization.When the optical RS data are unavailable or partly unavailable,the generated optical images can be alternative data that aid in land cover visual recognition for untrained people.To further promote the development of deep learning on remote sensing(RS),a paired SAR-optical image dataset based on Sentinel-1 and Sentinel-2 images is published and then used to evaluate proposed method.Proposed method can keep both the land cover and structure information well,and its performance is superior to some famous image-to-image translation models.The generated image can be used for optical RS image cloud removal and make up the optical RS image.In summary,the deep features and information representation of SAR images are studied based on deep learning.The fully supervised and unsupervised PolSAR image classification methods based on CNNs are developed.The SAR-to-optical image translation method based on S-Cycle GAN is also developed.All the methods achieve excellent performance on Gaofen-3 PolSAR images and the SAR-to-optical image translation method has already applied to the information fusion project.