Research On Remote Sensing Image Coding Techniques And Algorithms

As imaging sensor techniques advances to acquire more information on the desired imaging objects, the acquired image volume and its spatial and spectral resolutions increase significantly. Consequently, the coding, storage, transmission and the access of such large image data sets exceed the existing processing efficiency of the remote sensing encoder systems and the capabilities of their image codec.Generally, Remote Sensing (RS) signal compression aims at reducing the size of the signals which are acquired by a sensor or a scanner. In the image coding literature, many researchers addressed the problem of 2-D or 3-D image compression in many prospective. Consequently, many coding algorithms such as SPIHT, EBCOT and the related techniques and standards were developed. Then, what makes this research's roots different among other common image compression research?This research addresses not only the size reduction of 2-D and 3-D images but also investigates the drawbacks, complexities of the existing image coding technology and finds convenient solutions. These convenient solutions aim at efficient and information preserving coding of the three varieties of RS image categories under the three important interrelated RS areas. The three image categories are: 1) Low texture RS images, 2) Hyperspectral RS images, and 3) Rich Texture(RT) images with two kinds of image features; i) RT images with straight edges (RT-SE) and ii) RT images with contour shaped object boundaries (RT-CB). The three important RS areas deal with the enhancements of: ROI image coding, EBCOT based JPEG 2000 techniques and Transforms based RT image coding.Such consideration makes it possible to address the following six significant RS image coding issues and requirements.(1) The need of introducing an efficient, low complex and easy to modify ROI coding technique convenient for low texture 2-D RS images, (2) The need of proposing a Multitasking algorithm for time and cost efficient Hyperspectral image ROI selection and the lossless coding, (3) To explore the feasibility of currently popular baseline ROI codec, (4) To explore and to use the advanced key techniques of the EBCOT image coding algorithm based JPEG 2000 for solving the three issues deal with larger 2-D RS image coding and to eliminate the directional edge detail coding drawback of JPEG 2000, (5) The need of selecting efficient wavelet filters for RT-SE image coding while avoiding the complexities associated with the Wavelet Packet Transform(WPT), (6) The inefficiency problem of the existing transforms for information preserving coding of RT-CB remote sensing images are addressed and explained in the following paragraphs.The ROI image coding technology enhancements and research for RS are presented in Chapter 3. The key issues (1) and (2) are discussed bellow.(1) We propose an efficient, low complexity ROI image codec for compressing low texture (LT) RS images. It uses a convenient ROI coding concept, modified Spatial Orientation Trees (SOT) of wavelet coefficients, an optimal decomposition level for the SPIHT based LT image ROI codec. The set of wavelet filters used ROI codec efficiency explorations reveal that for faster RS image ROI coding tasks, the Symlet-4 adaptation performs better. The RD performance result outperformed that of the EBCOT based JPEG 2000, fairly at low bit rate. (2) The related problems of choosing and coding a ROI image efficiently without using many expensive Hyperspectral imaging missions has arisen with the major practical limitations such as massiveness of the acquired images versus limited onboard capacity, downlink contact time, etc. Therefore, the Multitasking algorithm is proposed in order to initially select the needy ROI with the low bit rate fast preview and with reference to the images in the size reduced storage archive. Then, on the ROI coding path, it addressed the drawbacks of the existing Hyperspectral image ROI coding technology using the de-coupled integer wavelet transform (IWT), Asymmetric Tree(AT) 3-D SPIHT based modified ROI coding technique. The RD performance of the proposed ROI coding technique outperformed that of the conventional 3-D SPIHT based technique.In Chapter 4, the EBCOT image coding algorithm based advanced JPEG 2000 technology is innovatively used for low resourceful RS encoder environment and the above key issue (4) is addressed. The ultimate outcomes are as follows.1) The use of the Precinct Partition image division strategy under the reversible image coding mode could achieve a maximum of 20 percent (at 0.75bpp) decrease of the encoder overall memory consumption than the use of Tiling Partition strategy under the reversible mode. 2) The 80% lowering of memory consumption at decoder and the immediate undersized on-ground preview image are achieved using the resolution prioritized progression ordering mode. 3) Using our enhanced algorithm with average layer thresholds, a sequence of different RS images is reconstructed at the same quality. 4) Despite of its many powerful features, the JPEG 2000 still lacks the RT image directional detail filtering capability due to its pre-defined wavelet transform phase. Our proposed enhancement for JPEG 2000 is named; Advanced Directional-Wavelet Packet Transform (ADWPT). The ADWPT is formed by eliminating the low frequency component filtering weakness of the Directional Filter bank Transform (DFT) associated with the Contourlet transform. The simulation results give competent performance over that of the popular, existing elementary coding techniques.Addressing the key issues (5) and (6), the transforms based coding technology enhancements for RT images are presented in Chapter 5. Initially, the new approach algorithm to select efficient wavelet filters for coding RT-SE images is formulated addressing the"Triple problem"of complexity associated with WPT based compression. This algorithm adapts the entropy-based criterion of energy threshold type as the cost function for the best basis selection. Then, a common compression for all the filters in the test set is achieved by setting the number of zeroed coefficients (NZC) as the independent parameter in the global energy thresholding module, which is a new technique. Starting with moderately lossy compression using NZC=88%, the objective performances of 11 convenient filters are observed and filter efficiency rankings are evaluated. The fairness of our new approach is validated by 10 filters out of 11 by establishing an assumption and performing the procedure of the new algorithm at NZC=50% lower lossy compression.RT-CB RS images have to be efficiently compressed and transmitted. Nevertheless, the popular wavelet transform (WT)s and the other existing transforms are less capable for the task. Therefore, in Chapter 5, the novel Contourlet Transform (CT) Combined Wavelet Packet Transform (CCWPT) is introduced; better subjective and objective coding results are obtained over the existing transforms based coding techniques. Furthermore, the SNR result outperforms that of the CT-DWT based combined technique available in the resent literature by 0.49 dB.