Attribute Learning for SAR Image Classification
نویسندگان
چکیده
منابع مشابه
Attribute Learning for SAR Image Classification
This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR) images. To explore the representative and discriminative attributes of SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a c...
متن کاملLearning from label proportions for SAR image classification
Synthetic aperture radar (SAR) image classification plays a key role in SAR interpretation. Due to the cost and difficulty of truth labeling for SAR images, the newly labeled samples available for image classification are very limited. This paper focuses on defining a new sample labeling method to solve the problem of truth acquisition for training data in SAR image classification. An efficient...
متن کاملSAR Image Segmentation Using Morphological Attribute Profiles
In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-...
متن کاملFully Polarimetric SAR Image Classification Using Different Learning Approaches
This paper compares multilook Polarimetric SAR (PolSAR) image classification using three types of learning: a supervised, an unsupervised and a semisupervised. The multilook PolSAR pixel values are complex covariance matrices and they are described by mixtures of Wishart distributions. Tests in synthetic and real images showed that the supervised and semisupervised classifications provided the ...
متن کاملHigh resolution SAR image classification
In this report we propose a novel classification algorithm for high and very high resolution synthetic aperture radar (SAR) amplitude images that combines the Markov random field approach to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done by dictionarybased stochastic expectation maximization amplitude...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ISPRS International Journal of Geo-Information
سال: 2017
ISSN: 2220-9964
DOI: 10.3390/ijgi6040111