Extraction of Soil and Water Conservation Measures Information from Remote Sensing Images Based on Image Segmentation Algorithm Protection Research

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Figure 3.

Information extraction segmentation parameter Settings

Argument Level		Ground floor	Second floor	Third floor	Fourth floor
Band weight	R	1	1	1	1
	G	1	1	1	1
	B	1	1	1	1
	green	1	1	1	1
	red	1	1	1	1
	NirRed	1	1	1	1
	SWIR	0.2	0.2	0.2	0.2
	VF	0.2	0.2	0.2	0.2
	dem	0.2	0.2	0.2	0.2
	Slope	0.2	0.2	0.2	0.2
Segmentation scale		50	80	120	170
colour		0.7	0.8	0.8	0.9
shape		0.5	0.4	0.4	0.3
smoothness		0.6	0.6	0.6	0.6
compactness		0.6	0.6	0.6	0.6

User accuracy evaluation table for each land type

Different grid size Land type	1m	5m	10m	15m	20m	25m	30m	35m
Sloping land	72.61	93.50	98.73	80.22	87.82	84.11	96.78	93.15
Terraced fields	98.33	97.36	98.18	98.12	97.38	97.37	98.92	97.59
damland	100.0	97.69	98.91	100.00	100.00	98.82	100.00	100.00
Sichuan plateau	99.96	100.00	98.38	100.00	100.00	99.84	100.00	100.00
Forest land	98.96	98.64	70.51	96.73	95.98	98.94	98.93	99.78
Open forest land	99.72	99.16	97.92	99.65	99.65	99.65	99.65	99.65
meadow	41.47	43.14	99.72	55.21	35.13	32.66	37.81	33.15
Water body	21.37	65.25	95.12	99.32	99.32	99.45	99.27	99.83
Settlement place	98.12	87.91	94.59	87.91	88.96	100.00	100.00	100.00
highroad	97.03	96.71	86.57	97.81	97.81	97.81	97.81	97.81
Wild grass land	99.53	99.61	99.61	99.61	99.61	99.61	99.61	99.61
Bare land	99.54	99.71	97.64	99.71	99.71	99.71	99.71	99.71

Evaluation table of cartographic accuracy of each land type

Different grid size Land type	1m	5m	10m	15m	20m	25m	30m	35m
Sloping land	99.51%	99.22%	74.39%	99.71%	99.29%	99.52%	99.19%	99.28%
Terraced fields	96.49%	96.49%	95.94%	96.49%	96.49%	96.49%	96.49%	96.49%
damland	99.61%	99.59%	99.13%	99.59%	99.59%	99.59%	99.59%	99.59%
Sichuan plateau	82.43%	82.43%	97.95%	82.43%	82.43%	98.25%	98.25%	98.25%
Forest land	75.69%	83.77%	98.62%	84.51%	78.29%	76.91%	86.11%	83.90%
Open forest land	99.57%	99.12%	98.37%	99.57%	99.57%	99.57%	99.57%	99.57%
meadow	99.35%	99.47%	32.87%	99.47%	99.47%	99.47%	99.47%	99.47%
Water body	98.41%	98.41%	97.72%	98.41%	98.41%	98.41%	98.41%	98.41%
Settlement place	95.91%	96.78%	96.78%	99.01%	96.78%	96.78%	96.78%	96.78%
highroad	97.91%	97.91%	87.29%	97.91%	97.91%	97.91%	97.91%	97.91%
Wild grass land	97.82%	98.53%	98.53%	98.53%	98.53%	98.53%	98.53%	98.53%
Bare land	99.29%	99.40%	99.40%	99.40%	99.40%	99.40%	99.40%	99.40%

Haralick texture feature parameters, mathematical definition and description

Parameter name	Mathematical definition	Description
homogeneity h	h=∑i,j=0N−1Pi,j1+(i−j)2	Describe the mean value of the image, that is, the degree to which the larger elements in the gray scale co-occurrence matrix are concentrated in the diagonal line. The more concentrated, the greater the homogeneity value, indicating the higher the mean value of the image
Contrast C	C=∑i,j=0N−1Pi,j(i−j)2	In contrast to homogeneity, it measures the degree of local variation of the image, and when the image varies greatly in the local range, the contrast value is also large
Dissimilarity degree d	d=∑i,j=0N−1Pi,j|i−j|	Linear correlation with contrast, the higher the local contrast, the greater the difference
Mean value u	ui,j=∑i,j=0N−1Pi,jN2	The average value is calculated by the joint occurrence frequency of the pixel values in the gray scale co-occurrence matrix and the adjacent pixel values
Standard deviation S	S=∑i,j=0N−1Pi,ji,j−ui,j	Similar to contrast and dissimilarity, standard deviation is expressed in the form of gray co-occurrence matrix, which is a measure of the deviation between pixel value and mean value
entropy e	e=∑i,j=0N−1Pi,j(−lnPi,j)2	When the texture in the image is inconsistent, the gray difference vector element value is small and the entropy value is large
Angular second moment a	a=∑i,j=0N−1Pi,j2	It describes the homogeneity and consistency of image gray distribution. When the image is homogeneous or consistent texture, the angular second order moment value is larger
correlation Co	Co=∑i,j=0N−1Pi,ji−uij−ujσi2•σj2	Measure the degree of linear dependence on the gray level of adjacent pixels and reflect the directionality of linear ground objects in the image. When linear ground objects are arranged in a certain direction, the correlation of this direction is higher than that of other directions
Angular second moment GLDV	∑k=0n−1V(k)2	The homogeneity, also known as energy, that describes the grayscale difference of an image
Entropy GLDV	∑k=0n−1−V(k)ln(V(k))	A measure of whether the texture features in an image are cluttered
Contrast GLDV	∑k=0n−1k2V(k)	Measure the local change degree of the image, and the contrast value is also large when the image is very large
Mean value GLDV	∑k=0n−1kV(k)	The average value is calculated by the joint occurrence frequency of the pixel values in the gray scale co-occurrence matrix and the adjacent pixel values