Land cover change detection using focused time delay neural network

The development of improved satellite technology generates a huge amount of remote sensing data, these data play the crucial role in natural resource management. The land use and land cover (LULC) change intensely affects local environment, as well as the global environment. Therefore, the quantifiable knowledge about LULC changes occur in global scale is important to make effective planning for conservation and precise use of natural resources, that has motivated the scientists to develop the various land cover change detection techniques. In this paper, we have proposed neural network-based approach, i.e., focused time delay neural network (FTDNN)-based approach for land cover change detection, which is a time series prediction-based approach and detect the sudden change in the enhanced vegetation index (EVI) time series. The performance of the proposed method has been addressed by using quantitative and qualitative analysis techniques. For the quantitative evaluation, the proposed algorithm is applied to the standard synthetic data set, which are analogous to EVI time series data set. The performance result of the proposed method compares with the four previously existing data mining-based benchmark techniques. The analysis was shown that the FTDNN-based method significantly outperforms than other techniques. For qualitative analysis, the San Francisco Bay Area data set has been used, which comprises real EVI time series. The proposed FTDNN-based method is applied to the San Francisco Bay Area data set and observe the interesting land cover changes. These outcomes indicate the effectiveness of data mining techniques for the land cover change detection problem.