IUNet-UCD: Improved U-Net with weighted binary cross-entropy loss function for urban change detection of Sentinel-2 satellite images

Abstract

Detecting changes in urban areas have always been an important element of urban planning and resource management. With the widening impacts of human activities on the ground terrain and landscapes, in recent decades the analysis of remote sensing data, including satellite images, has become a method of choice for rapid tracking of changes in the ground surface over wide geographical areas. However, this approach requires automated and semi-automated methods with the ability to detect changes in remote sensing data. In this study, Sentinel-2 satellite images taken in 2017 and 2021 were used for binary change detection in urban areas of ​​northern Iran. This change detection was performed with a supervised deep learning method, which was used to generate a detailed change map of the area. Considering the generally good performance of convolutional neural networks in change detection applications, this study used an improved U-net with a weighted binary cross-entropy loss function, this function considers unequal weights for positive and negative targets so it performs better in detecting changes in complex urban areas especially small changes rather than binary cross-entropy. In addition to Sentinel-2 satellite images of northern Iran, Onera Satellite Change Detection (OSCD) dataset was also used to evaluate our proposed method. Experimental results showed the good performance of the method on both datasets, with the accuracy and F1score of final change maps reaching above 97%.

Building Detection From Aerial Imagery Using Inception Resnet Unet and Unet Architectures

“…The development of remote sensing earth observation systems led to the availability of aerial images at almost all times and locations. It opened numerous applications in computer vision and photogrammetry, e.g., change detection (Gomroki et al, 2022, Isaienkov et al, 2021Zhang et al, 2020), long-term large-scale monitoring (Immerzeel et al, 2009;Lehmann et al, 2015), and urban management (Mignard and Nicolle, 2014). One of the vital elements that can be extracted from the aforementioned aerial images are buildings.…”