Fully Convolutional Siamese Autoencoder for Change Detection in UAV Aerial Images

Mesquita, Daniel B.; Santos, Rodrigo Fernandes dos; Macharet, Douglas G.; Campos, Mário F. M.

doi:10.1109/lgrs.2019.2945906

Cited by 35 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, some researchers have employed AEs to solve critical image processing challenges such as image classification [24][25][26], clustering [23,27,28], spectral unmixing [29] and image segmentation [30,31]. AEs have also been applied to deal with other important problems such as image fusion [32], change detection [33][34][35], pansharpening [36,37], anomaly detection [38,39], and image retrieval [40]. In recent years, the application of AE for clustering purposes has gained much attention in the RS community.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

et al. 2021

View full text Add to dashboard Cite

This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled dataset for their training step. As an alternative, in this paper, we developed an unsupervised learning model by employing a convolutional auto-encoder (CAE) to deal with the problem of limited labeled data for training. The CAE was used to learn and extract the abstract and high-level features without using training data. To assess the performance of the proposed approach, we used Sentinel-2 imagery and a digital elevation model (DEM) to map landslides in three different case studies in India, China, and Taiwan. Using minimum noise fraction (MNF) transformation, we reduced the multispectral dimension to three features containing more than 80% of scene information. Next, these features were stacked with slope data and NDVI as inputs to the CAE model. The Huber reconstruction loss was used to evaluate the inputs. We achieved reconstruction losses ranging from 0.10 to 0.147 for the MNF features, slope, and NDVI stack for all three study areas. The mini-batch K-means clustering method was used to cluster the features into two to five classes. To evaluate the impact of deep features on landslide detection, we first clustered a stack of MNF features, slope, and NDVI, then the same ones plus with the deep features. For all cases, clustering based on deep features provided the highest precision, recall, F1-score, and mean intersection over the union in landslide detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Different features are detected and localized into the scene, thus allowing to segment the region. In this application area, Siamese networks are used for change detection missions [50] but also for tracking tasks [51] by UAVs. Regardless, especially in civilian applications, UAVs equipped with vision systems are suitable for all those cases in which a quick intervention might be necessary while safeguarding the life of human beings [52], e.g., interventions in quarantine zones or critical environments.…”

Section: Introductionmentioning

confidence: 99%

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

et al. 2021

View full text Add to dashboard Cite

In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness.

show abstract

“…However, different classifiers have their own merits, and it is difficult to answer which classifier is suitable for a specific study [42]. In addition, deep neural networks (DNNs) have improved the accuracy of CD [52]- [58], which can extract abstract semantic features of the ground objects from massive data automatically and hierarchically [59] depending on datasets. Currently, there are some freely available data sets for CD, but the amount of open datasets is small, and some of them have small data sizes [52].…”

Section: Introductionmentioning

confidence: 99%

A Novel Change Detection Framework in Urban Area Using Multilevel Matching Feature and Automatic Sample Extraction Strategy

Zhou

Song

Cui

et al. 2021

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

View full text Add to dashboard Cite

Urban change detection (CD) using remote sensing images is of great significance for monitoring and analyzing the spatial and temporal distribution of changes within cities, which can be used to guide urban management. In order to reduce false detections and improve the CD automation, a novel CD framework for high resolution images is proposed in this paper. Firstly, a novel multi-level matching feature is presented, combining structural invariant features with multi-scale dense matching features to comprehensively describe the invariant properties of ground objects at different levels. Secondly, a newly automatic training sample extraction strategy is proposed, in which sufficient and accurate no-change samples can be obtained by Gaussian-weighted Dempster-Shafer evidence theory and L1norm, meanwhile, typical change samples can be extracted by sequential spectral change vector analysis. Utilizing the automatic extracted samples, the final CD results are obtained using four supervised classifiers respectively (k-nearest neighbor, support vector machine, rotation forest, and extra-trees). To validate the proposed CD framework, experiments are conducted in four datasets with spectral variability, spectral confusion between the changed objects and unchanged backgrounds, and misregistration. The results demonstrate that the proposed multi-level matching feature and automatic sample extraction strategy can obtain better results with different types of supervised classifiers, and can effectively improve automation of CD, which is applicable to the large spatial extent scene.

show abstract

Fully Convolutional Siamese Autoencoder for Change Detection in UAV Aerial Images

Cited by 35 publications

References 15 publications

Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

A Novel Change Detection Framework in Urban Area Using Multilevel Matching Feature and Automatic Sample Extraction Strategy

Contact Info

Product

Resources

About