Global intelligent system for waste disposal objects monitoring using the discrete orthogonal transformations based on neural network remote sensing image processing

Abstract

Remote sensing of the Earth allows to receive medium information, a high spatial resolution from space vehicles, and to conduct hyperspectral measurements. This study presents a remote sensing application using time-series Landsat satellite images to monitor the solid waste disposal site (WDS). The article proposes algorithms for working with spatial information, namely the transformation (convolution) of these manifolds into a one-dimensional sample. Recursive quasi-continuous sweeps are used for which the following conditions are satisfied: 1) preservation of the topological proximity of the original and expanded spaces, 2) preservation of correlations between the elements of the original and transformed spaces. An automated system is proposed for detecting and investigating waste objects based on the concept of fractal sets and convolutional neural networks. The first neural network detects WDS, the second works to localize the waste objects. This technique can become the object of further research on developing a medical-prophylactic expert system at the territorial level to detect and neutralize unauthorized waste disposal sites based on medium and highresolution space images. As a result, the proposed method demonstrates good accuracy in detecting the solid waste disposal site on real satellite images.