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API

Main Implementation

Equivalent Number of Looks (ENL) Analysis

Attributes

Classes

Functions:

equivalent_number_of_looks_analysis

Python
equivalent_number_of_looks_analysis(product: QualityInputProduct, roi_centers: list[tuple[int, int]], cropping_size: tuple[int, int]) -> list[ENLOutput]

Performing Equivalent Number of Looks Analysis on input product at each ROI center location.

Parameters:

Name Type Description Default
product QualityInputProduct

object satisfying the QualityInputProduct protocol

 required
roi_centers list[tuple[int, int]]

list of ROI centers where to perform the ENL computation, (range pixel, azimuth pixel)

 required
cropping_size tuple[int, int]

size of the ROI to be extracted, (number of range samples, number of azimuth lines)

 required

Returns:

Type Description
list[ENLOutput]

Equivalent Number of Looks results for each product channel and each ROI of interest

Utilities

Definition of ENL specific dataclasses

Classes

ENLOutput dataclass

Output results for Equivalent Number of Looks analysis

Attributes

product_name class-attribute instance-attribute
Python
product_name: str | None = None
channel class-attribute instance-attribute
Python
channel: str | None = None
swath class-attribute instance-attribute
Python
swath: str | None = None
polarization class-attribute instance-attribute
Python
polarization: SARPolarization | None = None
roi_center class-attribute instance-attribute
Python
roi_center: tuple[int, int] | None = None
roi_size_azimuth class-attribute instance-attribute
Python
roi_size_azimuth: int | None = None
roi_size_range class-attribute instance-attribute
Python
roi_size_range: int | None = None

Methods:

__init__
Python
__init__(product_name: str | None = None, channel: str | None = None, swath: str | None = None, polarization: SARPolarization | None = None, roi_center: tuple[int, int] | None = None, roi_size_azimuth: int | None = None, roi_size_range: int | None = None) -> None