Preprocessing

Preprocessing exports. More...

Functions
cvbres_t	CVSCubeAddition (CVCOMPOSITE ImageCube1, CVCOMPOSITE ImageCube2, cvbbool_t ClipResults, CVCOMPOSITE &ImageCubeOut)
	Element-wise addition of two Cubes. More...
cvbres_t	CVSCubeDivision (CVCOMPOSITE Dividend, CVCOMPOSITE Divisor, CVCOMPOSITE &ImageCubeOut)
	Element-wise division of two Cubes. More...
cvbres_t	CVSCubeMultiplication (CVCOMPOSITE ImageCube1, CVCOMPOSITE ImageCube2, cvbbool_t ClipResults, CVCOMPOSITE &ImageCubeOut)
	Element-wise multiplication of two Cubes. More...
cvbres_t	CVSCubeNormalize (CVCOMPOSITE ImageCubeIn, CVCOMPOSITE WhiteReference, CVCOMPOSITE BlackReference, CVSNormalizationMethod NormalizationMethod, CVCOMPOSITE &ImageCubeOut)
	Normalizes the Spectral cube. More...
cvbres_t	CVSCubeSubtraction (CVCOMPOSITE Minuend, CVCOMPOSITE Subtrahend, cvbbool_t ClipResults, CVCOMPOSITE &ImageCubeOut)
	Element-wise subtraction of two Cubes. More...

Detailed Description

Preprocessing exports.

Version

0.0.1.0

For details see function documentation.

Function Documentation

CVSCubeAddition()

cvbres_t CVSCubeAddition	(	CVCOMPOSITE	ImageCube1,
		CVCOMPOSITE	ImageCube2,
		cvbbool_t	ClipResults,
		CVCOMPOSITE &	ImageCubeOut
	)

Element-wise addition of two Cubes.

Creates a new Cube after adding all values of ImageCube1 and ImageCube2 for each element (ImageCube1 + ImageCube2).

Parameters

[in]	ImageCube1	Any kind of cube is supported as long as the dimensions match with ImageCube2
[in]	ImageCube2	Any kind of cube is supported as long as the dimensions match with ImageCube1
[in]	ClipResults	In case of TRUE: Clips the resulting pixel values to the maximum possible value of the resulting data type. In case of false the result might lead to overflow
[out]	ImageCubeOut	Linear cube of the same dimensions and data type. The metadata is a copy from ImageCube1

Returns

• CVC_ERROR (CVC_E_OK) on success
• CVC_ERROR (CVC_E_ERROR) otherwise

See also

CVSCubeMultiplication CVSCubeDivision CVSCubeSubtraction CVSCubeNormalize

CVSCubeDivision()

cvbres_t CVSCubeDivision	(	CVCOMPOSITE	Dividend,
		CVCOMPOSITE	Divisor,
		CVCOMPOSITE &	ImageCubeOut
	)

Element-wise division of two Cubes.

Creates a new Cube after dividing all values of Dividend and Divisor for each element (Dividend / Divisor).

Parameters

[in]	Dividend	Any kind of cube is supported as long as the dimensions match with Divisor
[in]	Divisor	Any kind of cube is supported as long as the dimensions match with Dividend
[out]	ImageCubeOut	Linear cube of the same dimensions of data type float. The metadata is a copy from Dividend

Returns

• CVC_ERROR (CVC_E_OK) on success
• CVC_ERROR (CVC_E_ERROR) otherwise

See also

CVSCubeMultiplication CVSCubeAddition CVSCubeSubtraction CVSCubeNormalize

CVSCubeMultiplication()

cvbres_t CVSCubeMultiplication	(	CVCOMPOSITE	ImageCube1,
		CVCOMPOSITE	ImageCube2,
		cvbbool_t	ClipResults,
		CVCOMPOSITE &	ImageCubeOut
	)

Element-wise multiplication of two Cubes.

Creates a new Cube after multiplying all values of ImageCube1 and ImageCube2 for each element.

Parameters

[in]	ImageCube1	Any kind of cube is supported as long as the dimensions match with ImageCube2
[in]	ImageCube2	Any kind of cube is supported as long as the dimensions match with ImageCube1
[in]	ClipResults	In case of TRUE: Clips the resulting pixel values to the maximum or minimum possible value of the resulting data type. In case of false the result might lead to overflow
[out]	ImageCubeOut	Linear cube of the same dimensions and data type. The metadata is a copy from ImageCube1

Returns

• CVC_ERROR (CVC_E_OK) on success
• CVC_ERROR (CVC_E_ERROR) otherwise

See also

CVSCubeDivision CVSCubeAddition CVSCubeSubtraction CVSCubeNormalize

CVSCubeNormalize()

cvbres_t CVSCubeNormalize	(	CVCOMPOSITE	ImageCubeIn,
		CVCOMPOSITE	WhiteReference,
		CVCOMPOSITE	BlackReference,
		CVSNormalizationMethod	NormalizationMethod,
		CVCOMPOSITE &	ImageCubeOut
	)

Normalizes the Spectral cube.

This function creates a normalized cube using the white- and black reference and the object cube. More details in CVSNormalizationMethod

Note

This function does not support stacked cubes (CVSCubeType = CVSCT_StackedCube). If you want to use this function anyhow, duplicate your cube with CVSCreateDuplicatedContinuousCube to get a continous cube.

Parameters

[in]	ImageCubeIn	Spectral Cube to work on
[in]	WhiteReference	The white reference contains a linear Cube with matching dimensions for samples and bands to ImageCubeIn
[in]	BlackReference	The black reference contains a linear Cube with matching dimensions for samples and bands to ImageCubeIn
[in]	NormalizationMethod	Defines the normalization method. More detailed information in CVSNormalizationMethod
[out]	ImageCubeOut	Normalized image of data type float32

Returns

• CVC_ERROR (CVC_E_OK) on success
• CVC_ERROR (CVC_E_ERROR) otherwise

See also

CVSCreateDuplicatedContinuousCube CVSCubeGetType CVSCubeMultiplication CVSCubeDivision CVSCubeAddition CVSCubeSubtraction

CVSCubeSubtraction()

cvbres_t CVSCubeSubtraction	(	CVCOMPOSITE	Minuend,
		CVCOMPOSITE	Subtrahend,
		cvbbool_t	ClipResults,
		CVCOMPOSITE &	ImageCubeOut
	)

Element-wise subtraction of two Cubes.

Creates a new Cube after subtracting all values of Minuend and Subtrahend for each element (Minuend - Subtrahend).

Parameters

[in]	Minuend	Any kind of cube is supported as long as the dimensions match with Subtrahend
[in]	Subtrahend	Any kind of cube is supported as long as the dimensions match with Minuend
[in]	ClipResults	In case of TRUE: Clips the resulting pixel values to the minimum possible value of the resulting data type. In case of false the result might lead to overflow
[out]	ImageCubeOut	Linear cube of the same dimensions and data type. The metadata is a copy from Minuend

Returns

• CVC_ERROR (CVC_E_OK) on success
• CVC_ERROR (CVC_E_ERROR) otherwise

See also

CVSCubeAddition CVSCubeMultiplication CVSCubeDivision CVSCubeNormalize