Adaptive convolutional neural network using N-gram for spatial object recognition.

Remote sensing applications are playing a vital role to improve the commercial satellite imagery with high resolution. In the spatial information system, object detection is the basic needs for computing the mathematical model. Geographical object related analysis for the image is used to gather data from remote sensing images. In this paper, we propose an Adaptive Convolutional Neural Network model using N-gram for Spatial Object Recognition on Satellite Images. Our methodology needs a learning model for the structures in the images to gather the data using prior knowledge. N-gram uses the functionalities of learning models. Spatial object recognition is performed using the learning method to segment the images with the human subjects that can increase their understanding of including the perception, cognition and decision. The result obtained for two stage of image processing is collected, and a relationship to psychological and mathematical basis is made. The results show that convinced association relevant level to the human perception is serving additional to identify the spatial objects. The experimentation is performed in MATLAB software where the results proved that our methodology is superior suitable for precise object detection and recognition on dissimilar levels of satellite images. [ABSTRACT FROM AUTHOR]