Artificial Neural Network Applications on Remotely Sensed Imagery Kaushik Das, Qin Ding, William Perrizo North Dakota State University

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

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…)

Spatial dataset example TIFF imageYield Map Spatial dataset can be viewed as collection of pixels, each having a value for each feature attribute 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.

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

Spatial Data Formats (Cont.) BAND ( ) ( ) ( ) ( ) BAND ( ) ( ) ( ) ( ) BSQ format (2 files) Band 1: Band 2:

Spatial Data Formats (Cont.) BAND ( ) ( ) ( ) ( ) BAND ( ) ( ) ( ) ( ) BSQ format (2 files) Band 1: Band 2: BIL format (1 file)

Spatial Data Formats (Cont.) BAND ( ) ( ) ( ) ( ) BAND ( ) ( ) ( ) ( ) BSQ format (2 files) Band 1: Band 2: BIL format (1 file) BIP format (1 file)

Spatial Data Formats (Cont.) BAND ( ) ( ) ( ) ( ) BAND ( ) ( ) ( ) ( ) BSQ format (2 files) Band 1: Band 2: BIL format (1 file) BIP format (1 file) bSQ format (16 files) B11 B12 B13 B14 B15 B16 B17 B18 B21 B22 B23 B24 B25 B26 B27 B

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).

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(K 1,..,K k, A 1, A 2, …, A n ) where K 1,..,K k are structure attributes and each A i 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)

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”.

An example of Ptree Peano or Z-ordering Pure (Pure-1/Pure-0) quadrant Root Count  Level  Fan-out  QID (Quadrant ID)

Ptree features Peano or Z-ordering Pure (Pure-1/Pure-0) quadrant Root Count  Level  Fan-out  QID (Quadrant ID) ( 7, 1 ) ( 111, 001 )  Level-0  Level-3  Level-2  Level-1

Basic, Value and Tuple Ptrees Value Ptrees (i.e., P 1, 001 = P 11 ’ AND P 12 ’ AND P 13 ) Tuple Ptrees (i.e., P 001, 010, 111 = P 1, 001 AND P 2, 010 AND P 3, 111 ) AND Basic Ptrees (i.e., P 11, P 12, …, P 18, P 21, …, P 28, …, P 71, …, P 78 )

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.

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

System Architecture Client server architecture Using CORBA as the backbone

System Screen Layout

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

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.

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.