Automatic Semantic Segmentation and Classification of Remote Sensing Image Data for Flood Detection Using Novel LSTM Neural Network - Sonavale, Amruta; Chakkaravarthy, Midhun; Srinivasa Rao, Surampudi; Salleh, Hishamuddin Bin M.; Jadhav, Jagannath (SN computer science, 2024)
Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite multispectral images have limited bands, low resolution, and information that can be analyzed. The bands are merged to form a unified image that incorporates data from all bands. However, current contouring techniques are affected by chromatic aberrations. This study uses intensity-hue-saturation and higher-order statistics to enhance the spatial and spectral information of remote sensing images in combination with segmentation and classification methods. High performance can be shown by implementing edge detection technology to identify objects using structure in Remote Sensing Images (RSI). The parameters used to evaluate the implementation of the proposed edge detection method included root mean square error, correlation coefficient, structural similarity index measure, and the error associated with the mean spectral analysis. The dimensionality of multiband RSI can be reduced based on higher-order data statistics using independent component analysis. Furthermore, images can be clustered using high-resolution panchromatic RSI models. The proposed technique can convert the pixel powers into the sign powers of adjacent higher-order partitions during region segmentation. Pixel intensities can be analyzed using preprocessing techniques such as denoising, which gradually stabilizes the object’s mean value. This method is shown not to impact the original seed level. In this proposed work wiener filtering is usedfornoisesuppressionintheremote-sensingimage.Afterthesecondstageofthe process, the structural region is extracted from the preprocessing image. After detecting structural regions in the RSI, the different feature values are obtained for further classification. The proposed Long Short-Term Memory (LSTM) neural network algorithm was used to predict the flood. Implementing the proposed LSTM neural network algorithm can automatically transform large-scale data sets and implement non-linear decision-making functions through the histological characteristics of multiple layers of neurons. This work compares the accuracy of flood forecasting in the collected data set using the proposed LSTM neural network algorithm. The performance of the proposed technology is analyzed in terms of accuracy, sensitivity, and specificity.