Change Detection from Unlabeled Remote Sensing Images Using SIAMESE ANN

Hedjam, Rachid; Abdesselam, Abdelhamid; Melgani, Farid

doi:10.1109/igarss.2019.8898672

Cited by 13 publications

(8 citation statements)

References 6 publications

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“…According to whether the weights of sub-networks are shared, this can be divided into the pure-Siamese structure [22,68,94,117,155,156] and the pseudo-Siamese structure [79,109,157,158]. The main difference is that the former sub-network extracts the common features of the two-period data by sharing weights.…”

Section: Siamese Structurementioning

confidence: 99%

“…Although unsupervised change detection does not require labeled training samples, sometimes the lack of prior knowledge makes it unsuitable for change detection involving semantic information. Weakly and semi-supervised schemes use inaccurate or insufficient labeled samples as a priori knowledge to solve this problem, which can be implemented with label aggregation [97], iterative learning [58,185], deep generative models [186] (see Section 5.6 for a more detailed review), sample generation strategies [156], or novel cost functions [36,187].…”

Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

“…Satellite images [129,214,233] Damage assessment a. Satellite images (caused by tsunami [190,234], particular incident [156], flood [235], or earthquake [19]) b. Aerial images (caused by tsunami [92]) c. SAR images (caused by fires [104], or earthquake [110]) d. Street view images (caused by tsunami [119]) e. Street view images and GIS map (caused by tsunami [236])…”

Section: Applications Data Typesmentioning

confidence: 99%

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Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

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Section: Siamese Structurementioning

confidence: 99%

Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

Section: Applications Data Typesmentioning

confidence: 99%

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Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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“…These techniques are becoming increasingly important due to their effectiveness in automatic learning of discriminative features. Recently, a number of DL-based change detection techniques have been proposed to solve CD problems, including fusion feature extraction and deep CNN (convolutional Neural Network) [12], deep belief network with fuzzy antologies and multiscale analysis [13], recurrent neural networks and Long-Short-Term Memory (LSTM) [14], dual-dense convolutional network (DCN) [15], deep CNN for multimodal remote sensing images [16], semi-Supervised Siamese ANN [17], to name a few of them.…”

Section: Introductionmentioning

confidence: 99%

Change Detection in Unlabeled Optical Remote Sensing Data Using Siamese CNN

Hedjam

Abdesselam

Melgani

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this article, we propose a new semi-supervised method to detect the changes occurring in a geographical area after a major damage. We detect the changes by processing a pair of optical remote sensing images. The proposed method adopts a patch-based approach, whereby we use a Siamese CNN (S-CNN), trained with augmented data, to compare successive pairs of patches obtained from the input images. The main contribution of this work lies in developing a S-CNN training phase without resorting to class labels that are actually not available from the input images. We train the S-CNN using genuine and impostor patch-pairs defined in a semi-supervised way from the input images. We tested the proposed change detection model on four real datasets and compared its performance to those of two existing models. Obtained results were very promising. Index Terms-remote sensing change detection. Siamese CNN. Semi-supervised change detection.

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“…Finally, in recent years, the combinations of unsupervised and supervised ML methodologies, namely called semi-supervised, have been proposed [126,[128][129][130][131]. The scope of these methods is to integrate the merit of both unsupervised and supervised methods to extrapolate information from RS data and construct new helpful knowledge.…”

mentioning

confidence: 99%

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

et al. 2022

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This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B sensors of the European (EU) Copernicus constellation allows fast mapping of damage after a disastrous event using radar data. In this research, we address the role of SAR (amplitude) backscattered signal variations for CD analyses when a natural (e.g., a fire, a flash flood, etc.) or a human-induced (disastrous) event occurs. Then, we consider the additional pieces of information that can be recovered by comparing interferometric coherence maps related to couples of SAR images collected between a principal disastrous event date. This work is mainly concerned with investigating the capability of different coherent/incoherent change detection indices (CDIs) and their mutual interactions for the rapid mapping of “changed” areas. In this context, artificial intelligence (AI) algorithms have been demonstrated to be beneficial for handling the different information coming from coherent/incoherent CDIs in a unique corpus. Specifically, we used CDIs that synthetically describe ground surface changes associated with a disaster event (i.e., the pre-, cross-, and post-disaster phases), based on the generation of sigma nought and InSAR coherence maps. Then, we trained a random forest (RF) to produce CD maps and study the impact on the final binary decision (changed/unchanged) of the different layers representing the available synthetic CDIs. The proposed strategy was effective for quickly assessing damage using SAR data and can be applied in several contexts. Experiments were conducted to monitor wildfire’s effects in the 2021 summer season in Italy, considering two case studies in Sardinia and Sicily. Another experiment was also carried out on the coastal city of Houston, Texas, the US, which was affected by a large flood in 2017; thus, demonstrating the validity of the proposed integrated method for fast mapping of flooded zones using SAR data.

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Change Detection from Unlabeled Remote Sensing Images Using SIAMESE ANN

Cited by 13 publications

References 6 publications

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Change Detection in Unlabeled Optical Remote Sensing Data Using Siamese CNN

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

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