Deep learning based remote sensing technique for environmental parameter retrieval and data fusion from physical models

摘要

Signal and data processing has established a new standard by using deep learning (DL) and deep neural network (DNN). This is done by obtaining advanced performance in terms of audio, image, and understanding of the language naturally. The major research in remote sensing has been given to the DL (deep learning) applications. The main application of deep learning in remote sensing is the classification of the data. The quality of the data received from the remote sensing sensors is insufficient. Thus, equal importance has to be given to the challenges associated with the magnification of these low-quality images. Addressing such challenges becomes predominant as it involves different environmental conditions, the tradeoff in the imaging system, and varying altitude images. This becomes the reason for the low quality of observations, thus making classification and identification a difficult task. Another huge challenge faced in the process of classification and identification is the heterogeneous nature of the remote sensing sensors. This also affects the efficiency and effectiveness of the data from remote sensing. The approaches for processing the remote sensing data can be improved by using the multi-modal datasets from the increasing sensing and additional secondary datasets when handy applications primary consideration. This makes researchers around the world get more interest in the fusion of data from multi-source for application diversity. The temporal data integration with spectral or spatial information is made possible using the spaceborne sensor's revisit capabilities. It helps in the representation of data structures with fresh data that is time variable, as well as data extraction techniques. Thus, this paper involves the development of the deep learning technique which is based on the remote sensing data for the extraction of features from the environmental parameters.