Research On Valid Region On-board Real-time Detection And Compression Technology Applied For Optical Remote Sensing Image

With the development of the national defense and economy,the related applications have put forward higher requirements on space optical remote sensing.The constant pursuit of the enhancement of the spatial,spectral,temporal and radiometric resolution of images has led to an exponential increase in the amount of data in onboard optical remote sensing images.The traditional remote sensing image processing methods suffer from the contradiction between the large amount of remote sensing images and the limited bandwidth of data transmission,which severely restricts the application and development of high-resolution optical remote sensing technology.In order to solve the bottleneck of the development and application of optical remote sensing satellites,this thesis firstly divides the remote sensing images into valid(nocloud-covered ground area,ship)and invalid regions(cloud-covered ground area,sea surface without ships)based on user applications,then proposes the on-board real-time detection and compression methods for the valid regions and explores the technical approaches for enhancing the effectiveness of the satellite image data transmission and increasing the camera imaging time per satellite orbit.The proposed methods can significantly improve the performance of the optical remote sensing satellite in the case of fixed data transmission bandwidth.Also,this thesis verifies the effectiveness of the proposed methods through simulation and hardware implementation.The research in this thesis is a new attempt to break through the existing on-board image data processing methods,and can provide the method and support for on-board processing of the optical remote sensing images in the future.Firstly,in this thesis we summarize the development of optical remote sensing image on-board compression and conclude that the traditional image compression methods cannot solve the contradiction between the remote sensing image applications and limited data transmission bandwidth.Thus we propose a new solution which combines the on-board real-time detection and compression,and removes the invalid regions to increase effectiveness of the satellite image data transmission and the camera imaging time per satellite orbit.We also analyze the related key technologies and extract the problems to be studied in this theirs.Secondly,we develop an overall research scheme of valid region on-board realtime detection and compression for the remote sensing images,which is designed based on two considerations: One is the characteristic analysis of remote sensing images,which results in a conclusion that utilization of different detection strategies for cloud/ship regions in different scenarios.The other is that the proposed compression method should fully integrate the advantage of the existing compression algorithms andthe satellite remote sensing image compression/decompression processing flow.Thus,we develop a new irregular image compression method under the integrated framework of the valid region real-time detection and compression,and present the feasible and realizable overall scheme.The invalid data of cloud-covered area in optical remote sensing images takes up a lot of storage space and transmission bandwidth,therefore,it is necessary to study the efficient,fast and accurate on-board cloud detection technology for remote sensing images,to support on-board real-time detection and compression scheme.In this thesis,for the high-performance classification of clouds and underlying surfaces in remote sensing images,a cloud detection method based on feature-space linear reduceddimensional transformation and a classification method that minimizes the number of support vectors are proposed.We convert the cloud detection into the classification problem between cloud and underlying surface.Based on the linear dimensionality reduction in multidimensional feature space,the correlation redundancy between the feature space parameters is effectively eliminate,and the classification performance of low-dimensional eigen space is also optimized.At the same time,we develop a classification method based on minimizing support vector number,which has better learning generalization performance and can distinguish clouds and underlying surfaces accurately.The proposed method can reduce the amount of computation while maintain the performance of cloud detection through feature dimensionality reduction strategy,and is more suitable for on-board implementation.The valid region of the sea scenario is the ship on the sea surface,and the invalid region of the large area can be removed by ship detection,thus the effectiveness of the downlink bandwidth can be greatly improved.In order to solve the problem of ship high-performance detection in remote sensing images,A ship detection method based on component geometric feature fusion discrimination is proposed.Firstly,considering the fusion of target geometry information and gray level distribution,we propose a suspicious target segmentation algorithm based on component tree and A-L criterion,which can realize the coarse target detection in complex cloud/sea scene,and effectively suppress complex background.Then,ship target detection algorithm based on feature distance discrimination is proposed.We compare the coarse detection results with the clustering features of the ship samples in the template library,further eliminate the number of the false alarms.The experimental results show that the algorithm has a strong ability to suppress sea clutter,islands,clouds and other disturbances,and can effectively support practical applications.After valid region detection and extraction,the images of ground targets and ships show irregular characteristics.For the compression problem of irregular images aftercloud extraction,a filling method based on local edge context and a shape-adaptive SACCSDS-IDC coding method are proposed.Based on the local correlation property in the image,an invalid region filling method based on connected region labeling and edge growth is constructed to regularize the irregular image.At the same time,the wavelet transform and bit-plane coding are improved under the wavelet domain processing strategy.In the process of scanning and coding,a shape-adaptive SA-CC SDS-IDC coding method based on shape-adaptive wavelet transform and irregular bit-plane scan was constructed.Experimental results show that the method can meet the requirements of irregular image processing and have high compression performance.Finally,considering the requirements of on-board real-time processing of massive images and the device reliability in space environment,we propose a hardware solution by using high performance commercial multi-core DSPs and FPGAs with reliability reinforcement design to meet space application requirements.At the same time,we construct a test platform of on-board remote sensing image real-time detection and compression processing based on VPX architecture.The performance of the processor is tested and evaluated through the test platform with some image data sets.The testing results show that the processor meets the design requirements in cloud detection,ship detection and real-time processing of irregular image compression.Compared with the traditional on-board image data processing,the valid region on-board real-time detection and compression can improve the effectiveness of the transmission of image data,increase the on-board imaging time of the camera,thereby greatly enhancing the application efficiency of the remote sensing satellite systems.The results lay a solid technical foundation for future application of on-board real-time detect and compression processing.