A NOVEL DEEP LEARNING METHOD FOR DETECTING CHANGES IN SATELLITE IMAGERY

The identification of change information has been crucial to the use of satellite imagery, the monitoring of land cover and use, the estimation of damage from natural catastrophes and the detection of military targets. There are numerous conventional techniques for detecting changes in multispectral remote sensing images, but they frequently fall short of our needs for durability, accuracy, and precision. This paper introduces a novel deep learning method for identifying changes in satellite data, with an emphasis on environmental changes and urban expansion. Using 24 pairs of Sentinel-2 satellite image data collected from 2015 to 2018, of which 10 pairs were used for testing and 14 pairs for training. Thirteen spectral bands with different spatial resolutions (10 m, 20 m, and 60 m) make up each multispectral image pair. The paper evaluates changes in urban and rural environments using the visible spectrum bands (2, 3, and 4) at a resolution of 10 m. The collection contains manually annotated changes. The paper compares the results obtained from the proposed solutions by Siamese Network and U-Net to address this change detection problem. With an accuracy of 0.86, the Siamese Network is used to detect high-level structural changes between pre- and post-event images by learning similarities between paired images. With an accuracy of 0.84, U-Net, which is intended for semantic segmentation, offers pixel-level predictions that improve change detection detail while the hybrid method for pixel-level change detection that combines the Siamese Network and U-Net in order to increase accuracy even further. This approach, which uses the Siamese Network for patch-wise similarity comparison and U-Net for fine-grained pixel segmentation, yields the maximum accuracy of 0.91. An efficient framework for applications in urban planning, crisis management, and environmental monitoring is suggested by the suggested hybrid technique, which shows great promise for accurate and thorough change detection in satellite imagery