Download Very High Resolution Georeferenced Satellite Images

High resolution satellite images are of utmost importance in many RS and GIS studies. However high resolution images are not available for free of cost. Hence it is often beyond reach for student and research community. While using google earth images for this purpose, often we end up in accuracy issues while carrying out georeferencing. This tutorial explains a method using Elshayal Smart GIS Application to download already georeferenced Very High Resolution Google Earth Image.

Watch till end to understand how to do it for larger region of interest and also to understand certain disadvantages.

The software directly takes the coordinates from Google Earth to georeference the image. However there are a few points to note,

• As the georeferencing is done automatically, you will not find any issue with accuracy or edge matching (However read the note and tips below)

• As the Images are from Google Earth, you will find only RGB bands. Since there will not be Infra Red band, these images have limited uses for applications like Normalized Differential Indeices, getting an FCC image etc.

• However these images can be used in applications, like Urban Planning and Development, Flood Monitoring etc. which need high resolution natural look images

• You can use these Images along with free Landsat or other images to use in any application

Below is the video on Downloading High Resolution Georeferenced Satellite Images using Elshayal (Smart GIS).

Note and Tips:

• In some cases edges may not match if caution is not exercised. While saving the image in Google Earth as shown in the video, take care that there is no shift in the image. Sometimes while saving the image, you will feel a small shift in the screen up or down (due to whatever reason), in which case edge matching will be an issue. So take care that there is not shift, if at all there is a shift you may have to redo the process.

• Make sure that Google Earth is connected to Elshayal (Smart GIS) before you save the image, else it will return an error.

• In some cases software says, Server is Busy. It is a small problem with Google Earth. What you all need to do is, close the GE, reopen it. Zoom to the level you want, make sure that the area you are looking forward is loaded completely. i.e, just pan your screen all over the place and make sure that all the area are in high quality(not blurred). Then do the usual process as shown in the video. It will work. It should do it automatically, but somehow in some versions of GE, as the next screen is loaded, the screen keeps buffering and doesn't automatically show up in High Resolution.

Supervised Classification in ERDAS Imagine

Classification is one of the very basic and important parts of Goespatial Technologies. Any satellite image will generally have 256 discrete values. Hence talking from layman’s point of view, every image will have around 256 classes. This implies that vegetation might be covered in 50 classes based on their DNs (As different type and stages of vegetation will have different spectral reflectance values).

Classification is basically a method which puts all these 50 values into 1 class of vegetation. So in general classification can be defined as the process of grouping all the pixels of an image into a specified number of classes.

Primarily there are two types of classification,

Unsupervised Classification: Discussed in unupervised Classification video in the blog.

Supervised Classification: This is type of classification that requires quite a bit of human intervention. Here the user will define something called signature set, which are primarily samples of the classes user is going to define. User will digitize a sample portion of a feature and then save it under s specific name. For example user will digitize a small potion of a waterbody and save it under the name water. Classifier will check the entire image and groups all the pixels with similar spectral properties of a specific spectral signature. This is the basics of Supervised Classification and as you can realise there is a need for the user to supervise the entire process. Resampling method selected will also affect the classification result to some extent.

Following is the video on Supervised Classification Using ERDAS Imagine.

Below is the video on classification if an image using ERDAS Imagine.

Notes and Tips:

• Accuracy of the classification only depends on the accuracy of the signature set. So take extra care while you define any signature
• If you are classifying the image into only a few classes say less than 5, may be unsupervised classification is the better solution
• If you feel there is mis-classification, you can edit the signature set and define a new class (or a class that is already existing, but you feel that the mis-classified portion should belong there) selecting the portion which you feel is mis-classified.  With this, you may end up having two classes of vegetation but likelihood of mis-classification will be addressed to some extent. While preparing map where you have more than one class for any feature, you can assign same colour to both the classes and keep only one class in the legend in the map

Unsupervised Classification using ERDAS Imagine

Classification is one of the very basic and important parts of Goespatial Technologies. Any satellite image will generally have 256 discrete values. Hence talking from layman’s point of view, every image will have around 256 classes. This implies that vegetation might be covered in 50 classes based on their DNs (As different type and stages of vegetation will have different spectral reflectance values). 

Classification is basically a method which puts all these 50 values into 1 class of vegetation. So in general classification can be defined as the process of grouping all the pixels of an image into a specified number of classes.

Primarily there are two types of classification,

Supervised Classification: Discussed in Supervised Classification video in the blog.

Unsupervised Classification: This is the simplest way of classifying an image, where human intervention is minimum. Here the user will just define the number of classes and there after we will not do any sort of supervision. System will classify the image based on the DN of the pixels into the number of classes defined by the user. A general comment may be made that, the DNs having same and close by values will be clubbed into one class. However the entire process that happens in the background is not so simple, it also varies slightly based on the re-sampling method selected.

Below is the video on classification if an image using ERDAS Imagine.

Notes and Tips:

• There are possibilities of mis-classification, for example, a portion of vegetation getting classified under the class water. If you find such a case, one method of addressing it to some level is to increase the number of classes. With this, you may end up having two classes of vegetation but likelihood of mis-classification will be addressed to some extent. While preparing map where you have more than one class for any feature, you can assign same color to both the classes and keep only one class in the legend in the map.
• Use unsupervised classification when you are looking for less number of classes. If you are looking for more number of classes, like more than 8 or 10, it is advisable to used supervised classification.

Generate Contours and DEM from Google Earth

Vitality of elevation information in the field of Geospatial technologies is a well known factor. This may be in the form of Spot Heights, Contours or DEM. Unfortunately the open source DEM we get is generally of spatial resolution of 30 meter and up. How many aware of the fact that the Elevation Information available in Google Earth can be utilized to get Spot Heights, Contours and DEM? Yes, the 3rd dimension information available in Google Earth can be manipulated to create Spot Heights, Contours and DEM in various GIS software using and small third party app. The app primarily gives you the elevation and all related information of each point that you have digitized on Google Earth in Excel format. Spot Heights can then be interpolated to get contours or DEM. Below is the link for the tutorial to create Contours and DEM from Google Earth.

Watch the video completely and understand the theory behind as well. If you are not looking for very high spatial resolution DEM, you can download ASTER DEM directly from earthexplorer or if you are from India you can download it from BHUVAN (I find that for Indian Region, CARTO DEM is better than ASTER).

Notes and Tips: 

•  Few people have asked me that altitude column is not getting updated. That is either problem with the installation or operating system. So try to re install tcx and try. Make sure that internet is connected then you do it. If it still doesn't work, try to do it in different operating system or different computer. Most of the people told it worked after doing this.

•  In the tutorial method is explained using SURFER to get final output. To do the process in ArcMap, follow the tutorial till getting excel file with elevation values, and then follow the other tutorial where it is explained how to create contours using spot heights (already available in the channel.)

•  Elevation information available in Google Earth is also interpolated values, so DEM or Contours extracted are as accurate as Google Earth Values.

Geo-Spatial Tutorial

Hello Geospatial Enthusiast,

Many of you visiting this blog might already be familiar with my Geo-Spatial Tutorials youtube channel. Remote Sensing and GIS is that vital part of Earth Science that can be applied to anything, anywhere and anytime. Though the spread of this technology is across the spectrum, the availability of the resources to effectively apply these are very scarce. Geo-Spatial Tutorials is a genuine effort to put many aspects and techniques of Remote Sensing and GIS together, free of cost, for the benefit of Earth Science fraternity.

I hold doctoral degree in remote Sensing and GIS. Here you will find the video tutorials on Remote Sensing, GIS and Photogrammetry.  Tutorials are primarily practical in nature, with small explanation on the theoretical part. However in the blog, along with every video I will be providing small write up on the theoretical part related to that video. Also few points and tips to note before you start trying, based on the E-mails I received from subscribers from youtube channel. Going through them will help in addressing the problems or questions those were faced by other fellow enthusiasts even before you face them.

Feel free to contact me on the E mail address provided for any question that you have. Generally I reply within 24 hrs. However please note that due to the large number of mails received, there might be a delay in response. Being a working professional, I make it a point to dedicate some amount of time on a daily basis to reply all mails. However due to the significant increase in the number of mails from both students and professional communities, I find it difficult to provide solutions to the problems that requires considerable amount of time and involvement. I will indicate the same in the reply to such mails. However for the queries those consume significant time and efforts, I will be happy to provide solution/consultation at mutually agreed consultation charges.

Do note that it is advisable to read the note provided along with the and also watch the video completely video prior to trying. Doing so will automatically answer many of the questions that would arise. Many of the mails that I receive are either due to not reading the points or not watching the video completely. Also do go through all the videos before asking for any video as it would already be present in the website.  These practices would help in effective and efficient use of time and resources.

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