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Artificial Neural Network Applications on Remotely Sensed Imagery

This study explores the use of Artificial Neural Networks to analyze remotely sensed imagery, specifically satellite images and aerial photography, for various applications such as precision agriculture. The study discusses the spatial data formats, including BSQ, BIL, BIP, and the newly proposed bSQ format, and introduces the concept of Peano-Count-tree (Ptree) for efficient data representation and mining.

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Artificial Neural Network Applications on Remotely Sensed Imagery

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  1. Artificial Neural Network Applications on Remotely Sensed Imagery Kaushik Das, Qin Ding, William Perrizo North Dakota State University kdas@globespan.com, qin.ding@ndsu.nodak.edu, william.perrizo@ndsu.nodak.edu

  2. Remotely Sensed Image (RSI) data • Satellite image / aerial photography • Landsat scenes covers 180 by 180 kilometers. • One scene for every place on earth every 18 days • Nearly a petabyte of data. • Valuable for precision agriculture. • A aerial photograph may cover a particular field (e.g., 800 by 800 meters). • Non-satellite imagery • Soil moisture • Nitrate concentration • Yield maps

  3. Spatial Data • Pixel – a point in a space • Band – feature attribute of the pixels • Value – we will assume all are one byte (0~255) • Images have different numbers of bands • TM4/5: 7 bands (B, G, R, NIR, MIR, TIR, MIR2) • TM7: 8 bands (B, G, R, NIR, MIR, TIR, MIR2, PC) • TIFF: 3 bands (B, G, R) • Ground data: individual bands (Yield, Moisture, Nitrate level, Temperature, elevation…)

  4. Spatial dataset example Spatial dataset can be viewed as collection of pixels, each having a value for each feature attribute TIFF image Yield Map For example, the spatial dataset above has 320 rows and 320 columns of pixels (102,400 pixels) and 4 feature attributes (B,G,R,Y). The (B,G,R) feature bands are in the TIFF image and the Y feature is color coded in the Yield Map.

  5. Spatial Data Formats • Existing formats • BSQ (Band Sequential) • BIL (Band Interleaved by Line) • BIP (Band Interleaved by Pixel) • New format • bSQ (bit Sequential)

  6. Spatial Data Formats (Cont.) • BAND-1 • 54 127 • (1111 1110) (0111 1111) • 4 193 • (0000 1110) (1100 0001) • BAND-2 • 7 240 • (0010 0101) (1111 0000) • 00 19 • (1100 1000) (0001 0011) BSQ format (2 files) Band 1: 254 127 14 193 Band 2: 37 240 200 19

  7. Spatial Data Formats (Cont.) • BAND-1 • 54 127 • (1111 1110) (0111 1111) • 4 193 • (0000 1110) (1100 0001) • BAND-2 • 7 240 • (0010 0101) (1111 0000) • 00 19 • (1100 1000) (0001 0011) BSQ format (2 files) Band 1: 254 127 14 193 Band 2: 37 240 200 19 BIL format (1 file) 254 127 37 240 14 193 200 19

  8. Spatial Data Formats (Cont.) • BAND-1 • 54 127 • (1111 1110) (0111 1111) • 4 193 • (0000 1110) (1100 0001) • BAND-2 • 7 240 • (0010 0101) (1111 0000) • 00 19 • (1100 1000) (0001 0011) BSQ format (2 files) Band 1: 254 127 14 193 Band 2: 37 240 200 19 BIL format (1 file) 254 127 37 240 14 193 200 19 BIP format (1 file) 254 37 127 240 14 200 193 19

  9. bSQ format (16 files) B11 B12 B13 B14 B15 B16 B17 B18 B21 B22 B23 B24 B25 B26 B27 B28 1 1 1 1 1 1 1 0 0 0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 Spatial Data Formats (Cont.) • BAND-1 • 54 127 • (1111 1110) (0111 1111) • 4 193 • (0000 1110) (1100 0001) • BAND-2 • 7 240 • (0010 0101) (1111 0000) • 00 19 • (1100 1000) (0001 0011) BSQ format (2 files) Band 1: 254 127 14 193 Band 2: 37 240 200 19 BIL format (1 file) 254 127 37 240 14 193 200 19 BIP format (1 file) 254 37 127 240 14 200 193 19

  10. bSQ Format • Split each band into eight separate files, one for each bit position. • Reasons for using bSQ format • Different bits contribute to the value differently. • bSQ format facilitates the representation of a precision hierarchy (from 1 bit up to 8 bit precision). • bSQ format facilitates the creation of an efficient data mining-ready data structure, Peano-Count-tree (Ptree).

  11. The “tabular” formats (inverted list) • BSQ and bSQ are “tabular” formats • BSQ consist of a separate table for each feature band • bSQ consist of a separate table for each bit of each band • One can view it this way: • The data set is initially one table, R(K1,..,Kk, A1, A2, …, An) where K1,..,Kk are structure attributes and each Ai is a feature attribute. • The structure attributes of a 2-D spatial dataset are the X and Y coordinates of the pixels (rows). • The feature attributes are the bands, B,G,R, NIR, … • In BSQ we separate each feature into a separate file and suppress the structure attributes altogether (under the assumption that the pixels are always arranged in raster order. • In bSQ we separate each bit of each feature into a separate file (same raster order assumption)

  12. Peano Count Tree (P-tree) • P-tree represents spatial bSQ data bit-by-bit in a recursive quadrant-by-quadrant arrangement. • An P-tree is a lossless representation of the original data. • A P-tree is a compressed structure. • A P-tree is “count pre-computed”.

  13. 55 55 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 16 16 8 8 15 15 16 16 3 3 0 0 4 4 1 1 4 4 4 4 3 3 4 4 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 1 1 An example of Ptree • Peano or Z-ordering • Pure (Pure-1/Pure-0) quadrant • Root Count • Level • Fan-out • QID (Quadrant ID)

  14. 001 55 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 2 3 16 8 15 16 2 3 0 4 1 4 4 3 4 3 111 1 1 1 0 0 0 1 0 1 1 0 1 2 . 2 . 3 ( 7, 1 ) 10.10.11 ( 111, 001 ) Ptree features Level-3 • Peano or Z-ordering • Pure (Pure-1/Pure-0) quadrant • Root Count Level-2 Level-1 Level-0 • Level • Fan-out • QID (Quadrant ID)

  15. Basic, Value and Tuple Ptrees Basic Ptrees (i.e., P11, P12, …, P18, P21, …, P28, …, P71, …, P78) AND Value Ptrees (i.e., P1, 001 = P11’ AND P12’ AND P13) AND Tuple Ptrees (i.e., P001, 010, 111 = P1, 001 AND P2, 010 AND P3, 111)

  16. Self-organizing Map (SOM) application • SOM – a special class of Artificial Neural Networks • Competitive learning – only one winner neuron per group • SOM can gives an intuitive two-dimensional map of a spatial data set in P-tree format.

  17. Goal • Use SOM to cluster yield attribute into high, medium and low yield regions. • Create pointers from cluster points to the corresponding areas of an aerial photo. • Derive association rules from the SOM map

  18. System Architecture • Client server architecture • Using CORBA as the backbone

  19. System Screen Layout

  20. Generated SOM from the image 29NW072894 Low Yield Medium Yield High Yield

  21. Advantages of using CORBA • We can add more servers easily. • CORBA is a standard. More services are provided. • Security • Dynamic method invocation • Multi-threaded service • Makes code efficient and clean. • CORBA + XML constitute a rudimentary “single server view of the network as discussed by Dr. Mochida.

  22. Conclusion • Considered new data structures for data mining and a clustering application. • Use wavelet for data preprocessing. • Generate SOM and cluster the yield map into high, medium and low yield regions.

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