Tool Name	Logistic Regression Classification
Tool ID	12
Library ID	imagery_opencv
Version	1.0
Author(s)	O.Conrad (c) 2019

Description

Integration of the OpenCV Machine Learning library for Logistic Regression based classification of gridded features.

Optimization algorithms like Batch Gradient Descent and Mini-Batch Gradient Descent are supported in Logistic Regression. It is important that we mention the number of iterations these optimization algorithms have to run. The number of iterations can be thought as number of steps taken and learning rate specifies if it is a long step or a short step. This and previous parameter define how fast we arrive at a possible solution.

In order to compensate for overfitting regularization can be performed. (L1 or L2 norm).

Logistic regression implementation provides a choice of two training methods with Batch Gradient Descent or the Mini-Batch Gradient Descent.

References

OpenCV - Open Source Computer Vision

OpenCV - Machine Learning Overview

Parameters

Name	Type	Identifier	Description	Constraints
Input
Features	grid list, input	FEATURES
Training Samples	table, input	TRAIN_SAMPLES	Provide a class identifier in the first field followed by sample data corresponding to the input feature grids.
Training Areas	shapes, input	TRAIN_AREAS
Output
Classification	grid, output	CLASSES
Look-up Table	table, output, optional	CLASSES_LUT	A reference list of the grid values that have been assigned to the training classes.
Options
Normalize	boolean	NORMALIZE		Default: 0
Training	choice	MODEL_TRAIN		Available Choices: [0] training areas [1] training samples [2] load from file Default: 0
Class Identifier	table field	TRAIN_CLASS
Buffer Size	floating point number	TRAIN_BUFFER	For non-polygon type training areas, creates a buffer with a diameter of specified size.	Minimum: 0.000000 Default: 1.000000
Load Model	file path	MODEL_LOAD	Use a model previously stored to file.
Save Model	file path	MODEL_SAVE	Stores model to file to be used for subsequent classifications instead of training areas.
Learning Rate	floating point number	LOGR_LEARNING_RATE	The learning rate determines how fast we approach the solution.	Minimum: 0.000000 Default: 1.000000
Number of Iterations	integer number	LOGR_ITERATIONS		Minimum: 1 Default: 300
Regularization	choice	LOGR_REGULARIZATION		Available Choices: [0] disabled [1] L1 norm [2] L2 norm Default: 0
Training Method	choice	LOGR_TRAIN_METHOD		Available Choices: [0] Batch Gradient Descent [1] Mini-Batch Gradient Descent Default: 0
Mini-Batch Size	integer number	LOGR_MINIBATCH_SIZE		Minimum: 1 Default: 1