We are analyzing and classifying the Land type using texture and color analysis.
From different images we can observe the color intensity mean values. and Can find the dominant color present. This will help to separate between some class like barren land vs. Forest. Here shown the Mean values of intensities for Different type of land images:
Color\Land | Agricultural | Barren | Forest | Houses | Mountains |
---|---|---|---|---|---|
Blue | 86 | 145 | 49 | 123 | 116 |
Green | 81 | 158 | 53 | 126 | 132 |
Red | 64 | 180 | 38 | 133 | 138 |
As from above table we observe that Genrally for forest land the Green part is dominant.
Properties\Land type | Mountains | Houses | Forest | Barren | Agricultura |
---|---|---|---|---|---|
Contras | 0.56 | 0.25 | 0.69 | 0.059 | 0.114 |
Correlation | 0.92 | 0.96 | 0.79 | 0.92 | 0.904 |
Energy | 0.05 | 0.08 | 0.08 | 0.44 | 0.32 |
Homogeneity | 0.78 | 0.88 | 0.74 | 0.97 | 0.94 |
Land type | Mountains | Houses | Forest | Barren | Agricultura |
---|---|---|---|---|---|
Entropy | 7.91 | 7.77 | 5.71 | 6.17 | 6.41 |
-> Here we start with, if the value for entropy is higher than some threshold we can say there are higher
probability of the land containing Mountains.
-> Than Secondly we find the highest present color in the image ,if it comes out to be Green than we can say the land
have forest.
-> We calculated Gray level co occurrence matrix(GLCM) and from that we found different properties(Contras,Correlation,Energy,Homogeneity)
-> Now we see that the lowest value occurs for contrast than it is equal chances for Barren land or aggricultural land.
-> Than we see if the dominant value of color is red than very likely the land is barren land. Else it is aggricultural land.
-> The Rest land can be treated as land containing Houses.
-> Here we pass the images having different type of land view.
-> Than we Find the Gray level co occurrence matrix(GLCM) for all the images.
-> Using GLCM we Found the properties of the Texture (Contras,Correlation,Energy,Homogeneity)
-> Here as we have values for all type of land so, we can compare in between them, which
can give a better result.
-> Here as for Mountains, the shadows and bright parts form a drastic change in intesity so it have highest value of contrast.
-> For the Houses we observe that correlation is very high because it show the inter relation between pixel to pixel.
-> Now For Barren land as there will be uniformity in the image so we observe high value of Homogeneity.
-> The remaining land we can classify as Aggricultiral land because it can contain mix properties of Barren land as well as Forest .