We present a new edge detector for automatic extraction of oceanograph
ic (mesoscale) features present in infrared (IR) images obtained from
the Advanced Very High Resolution Radiometer (AVHRR). Conventional edg
e detectors are very sensitive to edge fine structure, which makes it
difficult to distinguish the weak gradients that are useful in this ap
plication from noise. Mathematical morphology has been used in the pas
t to develop efficient and statistically robust edge detectors. Image
analysis techniques use the histogram for operations such as threshold
ing and edge extraction in a local neighborhood in the image. An effic
ient computational framework is discussed for extraction of mesoscale
features present in IR images. The technique presented here, the Histo
gram-Based Morphological Edge detector (HMED), extracts all the weak g
radients, yet retains the edge sharpness in the image. We also present
new morphological operations defined in the domain of the histogram o
f an image. We provide interesting experimental results from applying
the HMED technique to oceanographic data in which certain features are
known to have edge gradients of varying strength.