Classification of GIS image using GLCM and neural network

Abstract EN

GIS can hold agricultural regions data like forest, fruit covered lands and/or cultivate lands, these lands have been managed inside GIS by receiving a selected region remotely sensed image, so GIS users must have an appropriate digital map that represents these lands each one according to its owner, status, and some other data.

Normally, in such system, these lands will be classified by the users according to agricultural status depending on human vision.

So, hardly to the users to classify these lands manually, and this become a great problem which take a long time depending on human efforts, especially if there is a huge number of lands.

The suggested study creates a new Arc Map GIS tool which classifies these given lands automatically.

Thus, this tool runs the developed system application ; it will gather required information for each one of selected land, by sampling sub-images from their centers depending on the digital map, and gathers related status information from attribute database.

On the next stage, the system will extract a vector of textural features for each one of the selected lands from their image samples using second order statistics Gray Level Co-occurrence Matrix (GLCM) and calculate eight textural features for each one of three visible bands (RGB) for each land sample.

That vector of features will become the input to supervised multi-layer perceptron with backpropagation neural network classifier which be learned depending on recommended GIS user training data set.

As a result the system has accuracy near to 75 % ; these results were achieved by comparing the classification results from system test trials with desired user classification data