Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field.

Monitoring active fire (AF) utilizing remote sensing imagery provides critical support for fire rescue and environmental protection. Traditional methods for detecting active fires (AF) rely on the statistical analysis of AF radiance and background features. However, these algorithms are resource-intensive to develop and exhibit limited adaptability, particularly in distinguishing AF from interference pixels. Deep learning (DL) technologies, which can extract deep features from images, offer a new solution for efficiently detecting AF. This paper proposes an AF detection model based on convolutional neural networks (CNNs), named DAFDM. By integrating multi-layer features through an enhanced feature processing module (EFPM), the model produces high-quality AF information, accurately detecting AF from the background. Given the presence of uncorrected false alarms in the training labels, it is challenging for DL models to distinguish interference pixels, we construct a Landsat-8 dataset encompassing various fire types and interference objects, with precise labels. Comparing several architectures, we find that only U-Net type models can discern the AF boundary pixels fully and accurately. The proposed method outperforms other AF detection algorithms, achieving IoU and F1-score of 87.28% and 93.21%, respectively. Experimental results demonstrate that DAFDM possesses robust generalization capability in distinguishing interference pixels. The incorporation of land surface temperature (LST) as auxiliary data further improves DAFDM's performance, with interpretability methods employed to elucidate the impact of input data on predictions. This method is anticipated to further contribute to AF monitoring and wildfire development pattern analysis.

Polarimetric calibration is indispensable to quantitatively apply and analyze the polarimetric synthetic aperture radar (PolSAR) image. At present, the polarimetric calibration methods relying on the assumption of reflection symmetry have been widely used, which need to extract the reference targets that meet the assumptions before calibration and then calculate the cross-pol channel imbalance and crosstalk errors. However, the distortion in the uncalibrated image will affect the calculation of polarization features, resulting in inaccurate target extraction results. Consequently, we proposed a reflection symmetric target extraction method that combines with spatial statistics information. The method first takes the initial extraction result based on the polarization power total Span and introduces the hypothesis testing to judge the homogeneous samples. Finally, we automatically calculate the threshold by the Otsu algorithm to achieve high-precision extraction of the reflection symmetric targets. Meanwhile, we carried out the polarimetric calibration experiments based on real C- and X-band airborne PolSAR data and conducted qualitative and quantitative evaluation and analysis of the experimental results. The studies demonstrated that, compared with classical approaches, the proposed approach further improved the accuracy of polarimetric calibration by extracting more accurate reference samples.

Complications and Adverse Events Associated with Stent-Assisted Coiling of Wide-Neck Intracranial Aneurysms | InTechOpen, Published on: 2012-08-29. Authors: Xu Gao and Guobiao Liang