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Processing and Classification of Multichannel Remote Sensing Data

Processing and Classification of Multichannel Remote Sensing Data. Vladimir Lukin Nikolay Ponomarenko Andrey Kurekin Oleksiy Pogrebnyak. Outline. possible strategies of data processing are discussed one problem is to use more adequate models to describe the noise present in real images

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Processing and Classification of Multichannel Remote Sensing Data

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  1. Processing and Classification of Multichannel Remote Sensing Data Vladimir Lukin Nikolay Ponomarenko Andrey Kurekin Oleksiy Pogrebnyak

  2. Outline • possible strategies of data processing are discussed • one problem is to use more adequate models to describe the noise present in real images • another problem is automation of data processing, noise filtering and image compression before applying classification • some approaches that are effective and are able to perform well enough within automatic or semi-automatic frameworks for multichannel images are described and analyzed

  3. Strategies of On-board / On-land Processing • first strategy: lossy data compression without any pre- and post-filtering • second strategy: near-lossless data compression • third strategy: on-board pre-filtering. Then, pre-processed data are compressed in a lossy manner and passed downlink.

  4. Coders Used and Filtering Effect • Lossy image compression has a filtering effect • Two DCT-based coders with variable CR (varying QS) were used: • AGU: 16x16 block size, advanced probability models and deblocking filter • ADCT: partition scheme and variable block size from 4x4 to 32x32

  5. Optimal operation point • for different types of noise exists OOP in terms of CR for which SNR attains the maximum • for different metrics OOP (CR or bpp) can be slightly different • One practical problem was earlier how to reach this OOP when noise-free image is not available. However, this problem has been recently solved, at least, for coders controlled by QS, with C • Compression: non-overlapped blocks • Filtering: fully overlapped blocks

  6. Filtering vs Compression • Filtering: - fully overlapped blocks - quantization is applied only to relatively small DCT coefficients • Compression: - non-overlapped blocks - quantization of all DCT coefficients

  7. Classification of Compressed and Filtered Multichannel Images • Maximal SNR due to compression or filtering does not necessarily result in better classification • To study how conventional metrics that characterize efficiency of image filtering and compression are interconnected with quantitative measures that describe classification accuracy we have carried out special experiments.

  8. Test image • The test image has been formed using three channels (sub-bands) of Landsat TM image. These component images relate to central wavelengths 0.66 m, 0.56 m, and 0.49 m (optical bands) and they have been associated with R, G, and B

  9. Classification of test image • All components have been artificially corrupted by additive noise. • Five classes have been defined, namely, bare soil, grass, water surface, roads and urban areas, bushes. • Support vector machine (SVM) and radial basis function (RBF) neural network (NN) classifiers have been applied • Training has been performed for noise-free image. Training set contained about 10 times smaller number of samples than the validation set.

  10. Classification accuracy and compression ratio • The influence of lossy compression on classification accuracy has been examined for different compression ratios and two considered coders (AGU and ADCT) applied component-wise • Compression with different CR was applied to noisy test image (σ2=100)

  11. Compression and classification characteristics for different QS • AGU • ADCT

  12. Classification of different classes • Compression and classification characteristics for different classes (coder ADCT)

  13. Classification and filtering • Strategies 2 and 3 employ filtering • Two filters are considered: the component-wise DCT based filter with hard thresholding and 3D DCT filter

  14. Classification maps • Pc for the filtered images is almost the same as for noise-free classified data

  15. Conclusions • Simple case of pixel-wise classification of three-channel image corrupted by moderate intensity noise was considered • The presented example and analysis results show that both lossy compression (under condition of properly set parameters) and filtering can lead to positive effect of classification accuracy increasing due to noise reduction. • Noise intensity (and statistical characteristics) in component images of hyperspectral data vary in wide limits. Concerning practice, our recommendation is to apply pre- or post-filtering within strategies 2 and 3 if original SNR for a sub-band image is less than 35 dB. Otherwise, it is not worth applying any filter and the main task of compression is restricting of introduced distortions level with providing as large CR as possible.

  16. Future work • In practice, noise can be not pure additive. Quite complicated dependences of noise local variance on local mean have been observed for real life hyperspectral data • One needs more accurate blind estimators of noise characteristics and more careful approaches to lossy compression and filtering • Different homomorphic (variance stabilizing) transforms can be useful to simplify the situation

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