Can anyone suggest valuable open and free resources for learning Python for Remote Sensing, please?
I am studying about Remote Sensing, especially the application of remote sensing in land use land cover classification. I have a little bit of experience in python programing and am looking for a good resource for learning Python for image classification of various image data. Can anyone give me such a good resource, please? Thanks in advance.
There are several valuable open and free resources for learning Python for Remote Sensing and image classification:
The Python documentation https://docs.python.org/3/ provides a comprehensive overview of the language, including tutorials and examples.
The scikit-learn library https://scikit-learn.org/stable/ is a popular machine learning library for Python and includes tutorials and examples for image classification and other remote sensing applications.
The Earth Engine API https://developers.google.com/earth-engine provides a platform for remote sensing data analysis in Python and includes tutorials and examples for image classification and other applications.
The OpenCV library https://opencv.org/ is a popular computer vision library for Python and includes tutorials and examples for image classification and other remote sensing applications.
I think that ENVI (Environment for Visualizing Imagery) has a low entry level which allows you to get to image classification fast. The algorithms are state-of-the art for many applications including land use and land cover classification in supervised and unsupervised classification modes.
Hence have a look at the follwinf site to know more about this software, which I have been using for years now in remote sensing applications. It can also include Interactive Diagramming Language (IDL) when you need to program very specific appliations.
For image classification there are numerous tutorials are available by using Scipy and sklearn, however, the specialized tutorials on RS images are very less. Though, you can apply these with modification by converting RS image to numpy array. and covert back that numpy array to image using arcpy's RasterToNumPyArray
I think that ENVI (Environment for Visualizing Imagery) has a low entry level which allows you to get to image classification fast. The algorithms are state-of-the art for many applications including land use and land cover classification in supervised and unsupervised classification modes.
Hence have a look at the follwinf site to know more about this software, which I have been using for years now in remote sensing applications. It can also include Interactive Diagramming Language (IDL) when you need to program very specific appliations.
You can visit www.edx.org for the best Python and RS & GIS courses. A lot of good courses available there in free of cost. Below I have attached two links for Python and RS courses.
I think only way to learn anything is by doing that. you know, 'learn by doing'. So start doing some low level small projects; anything related to image processing in python. While doing you will learn so much from in-numerous online resources.
There are several valuable open and free resources for learning Python for Remote Sensing and image classification:
The Python documentation https://docs.python.org/3/ provides a comprehensive overview of the language, including tutorials and examples.
The scikit-learn library https://scikit-learn.org/stable/ is a popular machine learning library for Python and includes tutorials and examples for image classification and other remote sensing applications.
The Earth Engine API https://developers.google.com/earth-engine provides a platform for remote sensing data analysis in Python and includes tutorials and examples for image classification and other applications.
The OpenCV library https://opencv.org/ is a popular computer vision library for Python and includes tutorials and examples for image classification and other remote sensing applications.
It is important to describe misclassification errors in land cover maps and to quantify their propagation through geo-processing to resultant information products, such as land cover change maps. Geostatistical simulation is widely used in error modeling, as it can generate equal-probable realizations of the fields being considered, which can be su...
The value of any geodata set depends on its fitness for use. A critical measure of the fitness is the data quality, knowledge of which may significantly increase the confidence of the user in explaining and defending the results derived from analyses with the map (LMIC, 1999). Therefore, extensive information about the quality of geodata input to a...
To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the...