Problems in detecting and classifying sea surface anomalies by remote sensing
Ferdenant A. Mkrtchyan
V.A. Kotelnikov’ Institute of Radioengineering and Electronics, Russian Academy of Sciences, Fryazino, Moscow, Russia
E-mail: ferd47@mail.ru
Remote sensing is the collection of information on an object or area by a satellite orbiting the
earth, and classifying landforms and water bodies based on those remote measurements is one
of great importance. Various algorithms are developed for image recognition, then cluster
analysis is used to sort or classify the objects. Presently there are many ways to produce
recognition by developing a variety of algorithms. The problem of recognition starts when we
attempt to categorize some of the group of objects by class. . Information from remote sensing
is fed into a processing system which produces a two-dimensional image of the investigated
object. A statistical model of spottiness in the investigated area is developed from this type of
image. In real conditions, the investigation of spots, their statistical characteristics and their use
in detection is a complex procedure. So it is necessary to develop criteria allowing us to
distinguish the spots from other phenomena. A mathematical model parametrizing the
characteristics of sea surface spottiness is proposed in which the statistical characteristics for
“spottiness” of brightness temperatures in microwaves can be used in detecting and classifying
anomolies on the sea surface, caused by the degree of sea roughness.
The analysis of empirical histograms for spottiness of brightness temperatures in microwaves
shows that in most cases characteristics will be coordinated with an exponential distribution,
and amplitude characteristics will be coordinated with a normal distribution. Therefore, to
detect and classify anomalies on the sea surface it is necessary to apply optimal algorithms so
statistical distributions can be calculated. Numerical results of data processing from remote
sensing for the Arctic and Pacific regions are given.