Image analysis: Reclassify the pixels of an Image

To reclassify an image into classes could be useful when we need to investigate what is going on inside the cloud of pixels. At this time I am sharing an example on how to do that using Arcmap. The example shows the next step on the NDVI analysis presented in the previous post Acknowledgments are for Buitron and Fernandez (2102), and the copyrights statement is shown on the bottom of the page.

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ARCGIS and polygons (for Landsat imagery)

Objective:  To calculate the area of a polygon extracted from a Landsat scene, using ARCGIS 10. Credits as well as copyrights are the same of the previous entries (Buitrón and Fernandez, 2012).

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¿What is an unsupervised classification?

   What is an unsupervised classification?

     ¿Qué es la clasificación no supervisada?

      From an engineering perspective, the simplest use of optical remotely sensed imagery is on the interpretation and identification of historical changes of land surface features (e.g., on the area covered by a certain kind of forest). Using simple words, such task can be accomplished by classifying the pixels of the imagery into as many groups as needed, or more properly, as allowed by the resolution of the imagery.  There are two kinds of classification procedures: the unsupervised classification and the supervised classification. The supervised classification is principally used to extract qualitative information out from the imagery, having enough knowledge of the physical characteristics of some pixels, which can be used to characterize or classify other pixels within the study area. On the other hand, the unsupervised classification, simply generates clusters of pixels, that qualitatively characterize the study area. The latter is commonly used when there is not enough knowledge on the physical characteristics of the pixels (e.g., when performing multi temporal analysis of surface areas along a wide historical lapse of time, for which there is limited knowledge and/or information). An interesting example can be found following the link of the figure above. In this entry, I am presenting a tutorial of how to perform the latter (unsupervised classification). The material has been originally prepared by the consultants C. Buitrón and J. Fernández, under my supervision. This tutorial follows the series published on the entries presented before on this blog. Enjoy!

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Haze correction for Landsat imagery

This correction is optional. It can be used when considered that there is some influence of haze on the imagery. The example has been prepared using pre-loaded routines in ERDAS Imagine 2011.

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Atmospheric correction of Landsat imagery

Corrección atmosférica de imágenes Landsat

When an image is downloaded, besides the corresponding bands (which contain the data), you will notice that there are two txt files that accompany the data. Such files contain the Metadata necessary for the calibration of the image. The procedure you should follow is described in several papers, among which Chander et al. (2009) is highly recommended when using Landsat imagery. Next we describe the procedure we recommend to accomplish this task. The following is an example with a Landsat ETM scene, prepared by the ecologist and agricultural engineer C. Buitrón, under the assessment of the geographic engineer J. Fernández. Both are independent consultants in environmental projects.

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Image processing with ERDAS Part 2: Creation of an AOI (Area of Interest)

The next step in the unsupervised classification process is the selection of an Area of Interest, a.k.a. AOI in Erdas Imagine. The reasons to select an AOI are:

- To reduce the file size (very important when dealing with several images).

- To reduce the range of spatial variability, which is important when we need to apply a technique that considers ranges of variation of the values that characterize an area.

The tutorial presented in this post has been elaborated by the ecologist and agricultural engineer Carola Buitron, with the support of Jose Fernandez. The upcoming posts will show you the remaining steps to accomplish the task I mentioned. Enjoy and learn.

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Image processing Part I: Stacking multispectral bands using ERDAS

Satellite imagery can be used for several purposes. In environmental studies, its use has obvious advantages: low cost, large spatial coverage, and historical snapshots of a certain phenomena. In this series of posts, I am uploading a very well constructed tutorial on how to carry an unsupervised classification, for the study of multi temporal trends of a given geomorphological feature.

In this post, I am showing you how to stack multispectral bands in Erdas 2011. The tutorial has been elaborated by the ecologist and agricultural engineer Carola Buitron, supported by J. Fernandez and our team. The upcoming posts will show you the remaining steps to accomplish the task I mentioned. Enjoy and learn.

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