A Self-organizing Principle for Segmenting and Super-resolving ISAR Images
نویسندگان
چکیده
We present and illustrate the use of a bottleneck system for the segmentation and super-resolution of ISAR targets. The system is shown to be comprised of three basic subsystems: a compressing transformation, a bottleneck processor, and a decompressing transformation. We describe each subsystem and discuss the processing responsible for segmentation and super-resolution within this framework. Results using this network are assessed and issues regarding performance are introduced.
منابع مشابه
Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment
The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification) method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature v...
متن کاملKohonen Self Organizing for Automatic Identification of Cartographic Objects
Automatic identification and localization of cartographic objects in aerial and satellite images have gained increasing attention in recent years in digital photogrammetry and remote sensing. Although the automatic extraction of man made objects in essence is still an unresolved issue, the man made objects can be extracted from aerial photos and satellite images. Recently, the high-resolution s...
متن کاملA Method for Body Fat Composition Analysis in Abdominal Magnetic Resonance Images Via Self-Organizing Map Neural Network
Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in axial magnetic resonance (MR) images of the abdomen. Materials and Methods: A self-organizing map (SOM) neural network was designed to segment the adipose tissue from other tissues in the MR images. The segmentation of SAT and VA...
متن کاملHierarchical clustering of self-organizing maps for cloud classification
This paper presents a new method for segmenting multispectral satellite images. The proposed method is unsupervised and consists of two steps. During the rst step the pixels of a learning set are summarized by a set of codebook vectors using a Probabilistic Self-Organizing Map (PSOM, [9]) In a second step the codebook vectors of the map are clustered using Agglomerative Hierarchical Clustering ...
متن کاملUnsupervised construction of fuzzy measures through self-organizing feature maps and its application in color image segmentation
The paper presents a framework for the segmentation of multi-dimensional images, e.g., color, satellite, multi-sensory images, based on the employment of the fuzzy integral, which undertakes the classification of the input features. The framework makes use of a self-organizing feature map, whereby the coefficients of the fuzzy measure are determined. This process is unsupervised and therefore c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998