MDESNet: Multitask Difference-Enhanced Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images

Abstract

Building change detection is a prominent topic in remote sensing applications. Scholars have proposed a variety of fully-convolutional-network-based change detection methods for high-resolution remote sensing images, achieving impressive results on several building datasets. However, existing methods cannot solve the problem of pseudo-changes caused by factors such as “same object with different spectrums” and “different objects with same spectrums” in high-resolution remote sensing images because their networks are constructed using simple similarity measures. To increase the ability of the model to resist pseudo-changes and improve detection accuracy, we propose an improved method based on fully convolutional network, called multitask difference-enhanced Siamese network (MDESNet) for building change detection in high-resolution remote sensing images. We improved its feature extraction ability by adding semantic constraints and effectively utilized features while improving its recognition performance. Furthermore, we proposed a similarity measure combining concatenation and difference, called the feature difference enhancement (FDE) module, and designed comparative experiments to demonstrate its effectiveness in resisting pseudo-changes. Using the building change detection dataset (BCDD), we demonstrate that our method outperforms other state-of-the-art change detection methods, achieving the highest F1-score (0.9124) and OA (0.9874), indicating its advantages for high-resolution remote sensing image building change detection tasks.

Research on building instantiation change detection method in high spatial resolution remote sensing image

“…However, due to the small amount of information contained in the image, it is only suitable for a wide range of environmental monitoring and other research directions. As the technology containing the largest amount of information in remote sensing image, high-resolution remote sensing image has a wide range of applications [6][7][8][9][10] . Building change detection based on high resolution remote sensing image has become a research hotspot.…”

Multistage Interaction Network for Remote Sensing Change Detection

“…Previous studies [38][39][40] have utilized LSTM for acquiring change information. To address potential misinteraction, attention mechanisms have been introduced [41][42][43] to guide the network's focus on critical interactions. Additionally, Fang et al [44] emphasized the importance of temporal interaction during feature extraction.…”

A New High-Resolution Rural Built-Up Land Extraction Method Based on Artificial Surface Index with Short-Wave Infrared Downscaling

“…Machine learning algorithms have been widely used to acquire distribution and dynamical changes of built-up areas using medium-to high-resolution images [15][16][17]. In recent years, deep learning techniques have received much attention for building extraction from high-resolution or very-high-resolution (VHR) images [4,18,19]. Deep learning models achieve high accuracy, but due to the requirement of abundant high-quality training data, the demand for computational resources is large and data processing work is heavy [20,21].…”