Description

In this paper to discuss and analysis for land cultivation and possibilities for growth in land. We discuss about our image processing to be select training dataset of images to collect and proceed for the testing for soils. Further the soil some testing and classification for cultivation techniques availability and clarification for the results. In this process we done with MATLAB for image processing and used for feature extraction and segmentation for land analysis. For instance, the remotely sensed data can produce a map like the image for land use classification which is as the final product of analysis. Image classification it becomes an important tool for digital image classification. The classifier loosely refers to computer programs that perform specific procedures for classification of images. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel frame work to train coarse-to-fine shared intermediate representations, which are termed “sparse lets,” from a large number of pre trained part detectors. Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before.