A new clustering algorithm applicable to multispectral and polarimetric SAR images

Journal Article · Sat May 01 04:00:00 EDT 1993 · IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers); (United States)

DOI:https://doi.org/10.1109/36.225530· OSTI ID:7067893

Wong, Y F ^[1]; Posner, E C ^[2]

---

1. Lawrence Livermore National Lab., CA (United States). Inst. for Scientific Computing Research
2. California Inst. of Tech., Pasadena, CA (United States). Dept. of Electrical Engineering

The authors describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, the authors extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

• OSTI ID: 7067893
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers); (United States), Journal Name: IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers); (United States) Vol. 31:3; ISSN 0196-2892; ISSN IGRSD2
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
540210 -- Environment
Terrestrial-- Basic Studies-- (1990-)
99 GENERAL AND MISCELLANEOUS
990200* -- Mathematics & Computers
ALGORITHMS
CLASSIFICATION
CROPS
IMAGE PROCESSING
MATHEMATICAL LOGIC
MEASURING INSTRUMENTS
MULTISPECTRAL SCANNERS
PROCESSING
REMOTE SENSING