Refining Land-Cover Maps Based on Probabilistic Re-Classification in CCA Ordination Space
نویسندگان
چکیده
منابع مشابه
Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps
To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prep...
متن کاملSequential Land Cover Classification
Land cover classification using remotely sensed data is a critical first step in large-scale environmental monitoring, resource management and regional planning. The classification task is made difficult by severe atmospheric scattering and absorption, seasonal variation, spatial dependence, complex surface dynamics and geometries, and large intra-class variability. Most of the recent research ...
متن کاملLand Cover Classification based on Polarimetric Coherence Signatures
The paper presents a new form of polarimetric interferometric information visualization: polarimetric coherence signatures. These signatures combine the idea of conventional polarization signatures and the concept of interferometric coherence. The polarimetric coherence signature plot shows the magnitude of the interferometric coherence as a function of the polarization ellipse parameters (elli...
متن کاملLand Cover Classification Based on General Type-2 Fuzzy Classifiers
This paper proposes a fuzzy classifier based on type-2 fuzzy sets to be applied in land cover classification. The classifier is built on the basis of the available data and considers the merging of information drawn from different experts. The data regard a thematic mapper representing the land cover of a real plain cultivated area. The experts are represented by different bands which classify ...
متن کاملLand Cover Classification based on the Universal Pattern Decomposition Method
* Corresponding author. E-mail: [email protected] Abstract – The universal pattern decomposition method (UPDM) has been successfully applied to simulated data for Landsat/ETM+, Terra/MODIS, ADEOS-II/GLI and others using ground-measured data. The UPDM is tailored to decrease dimensions of hyper multi-spectral data that have sensor-independent characteristics and thus exploit hyper multi-s...
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: Remote Sensing
سال: 2020
ISSN: 2072-4292
DOI: 10.3390/rs12182954