Ks. Chen et al., CLASSIFICATION OF MULTIFREQUENCY POLARIMETRIC SAR IMAGERY USING A DYNAMIC LEARNING NEURAL-NETWORK, IEEE transactions on geoscience and remote sensing, 34(3), 1996, pp. 814-820
A practical method for extracting microwave backscatter for terrain-co
ver classification is presented in this paper. The test data are multi
frequency (P, L, C bands) polarimetric SBR data acquired by JPL over a
n agricultural area called ''Flevoland.'' The terrain covers include f
orest, water, bare soil, grass, and eight other types of crops. The ra
dar response of crop types to frequency and polarization states were a
nalyzed for classification based on three configurations: 1) multifreq
uency and single-polarization images; 2) single-frequency and multipol
arization images; and 3) multifrequency and multipolarization images.
A recently developed dynamic learning neural network was adopted as th
e classifier. Results show that using partial information, P-band mult
ipolarization images and multiband hh polarization images, have better
classification accuracy, while with a full configuration, namely, mul
tiband and multipolarization, gives the best discrimination capability
. The overall accuracy using the proposed method can be as high as 95%
with a total of thirteen cover types classified. Further reduction of
the data volume by means of correlation analysis was conducted to sin
gle out the minimum data channels required. It was found that this met
hod efficiently reduces the data volume while retaining highly accepta
ble classification accuracy.