A domain adaptation neural network for change detection with heterogeneous optical and SAR remote sensing images

Zhang, Chenxiao; Feng, Yukang; Hu, Lei; Tapete, Deodato; Pan, Li; Liang, Zheheng; Cigna, Francesca; Yue, Peng

doi:10.1016/j.jag.2022.102769

Cited by 24 publications

(10 citation statements)

References 21 publications

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“…The study [1] introduces a Dynamic Equilibrium Network (DENet) to enhance fire detection across varied domains, specifically targeting data from spaceborne, airborne, and terrestrial sensors. Tested on the Flame And Smoke Detection Dataset (FASDD) and the FLAME dataset, DENet showcased a remarkable balance in learning, evidenced by a 7.71% improvement in mean average precision (mAP) over the PODNet model on existing datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

FireDetXplainer: Decoding Wildfire Detection With Transparency and Explainable AI Insights

Rubab,

Ghaffar,

Choi

2024

IEEE Access

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Recent analyses by leading national wildfire and emergency monitoring agencies have highlighted an alarming trend: the impact of wildfire devastation has escalated to nearly three times that of a decade ago. To address this challenge, we propose FireDetXplainer (FDX), a robust deep-learning model that enhances the interpretability often lacking in current solutions. FDX employs an innovative approach, combining transfer learning and fine-tuning methodologies with the Learning without Forgetting (LwF) framework. A key aspect of our methodology is the utilization of the pre-trained MobileNetV3 model, renowned for its efficiency in image classification tasks. Through strategic adaptation and augmentation, we have achieved an exceptional classification accuracy of 99.91%. The model is further refined with convolutional blocks and advanced image pre-processing techniques, contributing to this high level of precision. Leveraging diverse datasets from Kaggle and Mendeley, FireDetXplainer incorporates Explainable AI (XAI) tools such as Gradient Weighted Class Activation Map (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME) for comprehensive result interpretation. Our extensive experimental results demonstrate that FireDetXplainer not only outperforms existing state-of-the-art models but does so with remarkable accuracy, making it a highly effective solution for interpretable image classification in wildfire management.

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Section: Literature Reviewmentioning

confidence: 99%

FireDetXplainer: Decoding Wildfire Detection With Transparency and Explainable AI Insights

Rubab,

Ghaffar,

Choi

2024

IEEE Access

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“…For instance, Li et al [237] proposed a GAN and CNN-based network for optical and SAR image CD, using GANs to align optical and SAR images into the same feature space, followed by supervised CNN for CD. Zhang et al [238] applied domain adaptation constraints to align optical and SAR images at a deep feature level within the same feature space, unifying deep heterogeneous feature alignment and CD tasks in an end-to-end framework, thereby avoiding unintended noise introduction.…”

Section: Multimodal CDmentioning

confidence: 99%

Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms

Wang,

Zhang,

Gao

et al. 2024

Remote Sensing

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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.

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“…Traditional image processing systems for remote sensing data are hardly capable of processing instantaneous image applications, making it difficult to fully use the extensive remote sensing image resources [2]. The geometric positioning of remote sensing images plays an important role in remote sensing image applications such as image fusion [3][4][5], change detection [6][7][8], image mosaicing [9][10][11], etc. The initial step and the biggest challenge is image matching.…”

Section: Introductionmentioning

confidence: 99%

An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database

2023

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Satellite remote sensing has entered the era of big data due to the increase in the number of remote sensing satellites and imaging modes. This presents significant challenges for the processing of remote sensing systems and will result in extremely high real-time data processing requirements. The effective and reliable geometric positioning of remote sensing images is the foundation of remote sensing applications. In this paper, we propose an optical remote sensing image matching method based on a simple stable feature database. This method entails building the stable feature database, extracting local invariant features that are comparatively stable from remote sensing images using an iterative matching strategy, and storing useful information about the features. Without reference images, the feature database-based matching approach potentially saves storage space for reference data while increasing image processing speed. To evaluate the performance of the feature database matching method, we train the feature database with various local invariant feature algorithms on different time phases of Gaofen-2 (GF-2) images. Furthermore, we carried out matching comparison experiments with various satellite images to confirm the viability and stability of the feature database-based matching method. In comparison with direct matching using the classical feature algorithm, the feature database-based matching method in this paper can essentially improve the correct rate of feature point matching by more than 30% and reduce the matching time by more than 40%. This method improves the accuracy and timeliness of image matching, potentially solves the problem of large storage space occupied by the reference data, and has great potential for fast matching of optical remote sensing images.

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A domain adaptation neural network for change detection with heterogeneous optical and SAR remote sensing images

Cited by 24 publications

References 21 publications

FireDetXplainer: Decoding Wildfire Detection With Transparency and Explainable AI Insights

FireDetXplainer: Decoding Wildfire Detection With Transparency and Explainable AI Insights

Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms

An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database

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