In recent years, identifying changes in multi-temporal images in terms of land use and land cover has been significant in a variety of applications, including urban planning. Due to weather and environmental effects, optical remote sensing has limitations in obtaining images where the image quality may be degraded. It’s because the images being registered are taken at various times, viewpoints, and types of sensors. In this article, the pre-processing methods, which include radiometric correction and geometric correction, are introduced to enhance the quality of satellite images and identify correct spatial alignment. For radiometric correction, adaptive contrast enhancement is done by combining histogram- and non-linear transfer function-based approaches in CIELAB color space. A comparison study is done to see how the new method compared to other methods. For geometric correction, the features from two images are extracted using Convolutional Neural Network to match and align them. The introduced approach for radiometric correction gave the best average rank of BRISQUE scores and RMSE of contrast scores, and the geometric correction can align two images together with an average accuracy of improvement of 91.78 percent. The findings of this research will provide the preliminary step for any change detection activities.
In recent years, identifying changes in multitemporal images in terms of land use and land cover is significant in a variety of applications including urban planning. CNN architectures are one of the most extensively utilised methods for change detection. The aim of this research is to investigate two types of skip connections that may be incorporated into CNN architecture to determine if they can improve the effectiveness of change detection during the CNN learning process. In this paper, we adopt the U-Net architecture to train the change detection model. We also modify the U-Net skip connection's path to include the dense skip connection and compare the modified U-Net with the original U-Net, which uses the plain skip connection. We also test the trained model with our collected local dataset in Cyberjaya to see how well it can anticipate changes in our location. The results of this study show that a U-Net with dense skip connections produces the best results and optimises change detection. It will help researchers understand how important the skip connection is to the model's performance.
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