What is a remote sensing change detection technique? Towards a conceptual framework

Salah, Hayet Si; Goldin, Sally E.; Rezgui, Abdelmounaam; Islam, Bachari Nour El; Ait-Aoudia, Samy

doi:10.1080/01431161.2019.1674463

Cited by 22 publications

(14 citation statements)

References 24 publications

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“…For detecting changes in land use/land cover, many issues have been discussed and synthesized in literature reviews of several researchers' articles [31,[33][34][35][36][37][38][39][40][41]. Salah et al [42] mentioned the different categorization schemes in previous reviews, which have usually focused on a few aspects or dimensions of change detection problems while ignoring others because of the complexity of this topic. However, Hemati et al [31] and Zhu [41] have suggested six categorical change detection methods such as thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression, which have been applied in diverse applications using Landsat data to observe the dynamics caused by human activities including urban evolution analysis [43,44].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, in this recent time-series approach, a set of satellite image scenes taken at many different times-Satellite Image Time Series (SITS) affords a large amount of information compared to a single image couple in the context of temporal tendencies of regional evolution [46]. Despite these benefits, it still raises specific challenges regarding: the irregular temporal phenological signature of different land cover types; the insufficient sampling used to train the supervised classification; the missing temporal data [42]; the network architectures or specific datasets shaping that need to be developed for exploiting the temporal information jointly with the spatial and spectral information of the data [47]. Thus, in a more classical way, other sets of approaches and methods can be used varying from manual change interpretation [48] to bi-temporal linear data transformation [49] or multi-temporal spectral mixture analysis [50] and deep learning [51].…”

Section: Introductionmentioning

confidence: 99%

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Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data

Nguyen

Dang

et al. 2022

Remote Sensing

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Socio-economic development has promoted the modification of land cover patterns in the coastal area of Ha Long, Cam Pha cities since the 1990s. The urban growth, together with intensive coal mining activities, has improved the life quality of residents. However, it has also caused many environmental problems in this region. Change detection techniques based on post-classification comparison were applied for monitoring the spatial and temporal evolution of land covers. The confusion matrix for 2001 and 2019 showed high overall accuracy (97.99%, 94.95%) and Kappa coefficient (0.97, 0.92), respectively. Statistics from classified images have revealed that man-made features increased by about 15.32%, while natural features, mangrove jungles, and water bodies decreased 10.64%, 1.96%, 2.72%, respectively, and urban evolution presents various dynamics, soft in the first period (1991–2001), but stronger in the second period (2001–2019) with different characteristics. The study also expresses the constraint of topographic and geologic resources, which have prevented the urban development in this coastal area. Such obtained results are very important for understanding interactions and relations between natural and human phenomena and they may help authorities by providing indicators and maps able to highlight necessary actions for sustainable development.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data

Nguyen

Dang

et al. 2022

Remote Sensing

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“…Change detection (CD) is one of the most active research areas in Remote Sensing (RS) [1,2]. Specifically, the term CD refers to the process of identifying areas of the Earth's surface that have experienced changes through the joint analysis of two or more co-registered images captured at different epochs [3,4,1,5].…”

Section: Introductionmentioning

confidence: 99%

Inferring 3D change detection from bitemporal optical images

Marsocci¹,

Coletta²,

Ravanelli³

et al. 2022

Preprint

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Change detection is one of the most active research areas in Remote Sensing (RS). Most of the recently developed change detection methods are based on deep learning (DL) algorithms. This kind of algorithms is generally focused on generating two-dimensional (2D) change maps, thus only identifying planimetric changes in land use/land cover (LULC) and not considering nor returning any information on the corresponding elevation changes. Our work goes one step further, proposing two novel networks, able to solve simultaneously the 2D and 3D CD tasks, and the 3DCD dataset, a novel and freely available dataset precisely designed for this multitask. Particularly, the aim of this work is to lay the foundations for the development of DL algorithms able to automatically infer an elevation (3D) CD map -together with a standard 2D CD map -, starting only from a pair of bitemporal optical images. The proposed architectures, to perform the task described before, consist of a transformer-based network, the MultiTask Bitemporal Images Transformer (MTBIT), and a deep convolutional network, the Siamese ResUNet (SUNet). Particularly, MTBIT is a transformerbased architecture, based on a semantic tokenizer. SUNet instead combines, in a siamese encoder, skip connections and residual layers to learn rich features, capable to solve efficiently the proposed task. These models are, thus, able to obtain 3D CD maps from two optical images taken at different time instants, without the need to rely directly on elevation data during the inference step. Encouraging results, obtained on the novel 3DCD dataset, are shown. The code and the 3DCD dataset are available at https://sites.google.com/uniroma1. it/3dchangedetection/home-page.

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“…Research in computer vision and artificial intelligence focused on remote sensing in disaster management has produced a vast corpus of tools and algorithms [11] for providing the analysis and interpretation of data needed by emergency responders. Models and algorithms to infer damage or change have become increasingly popular in aiding decision support as the technology has grown to support such computation.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Semantic Inference of Individual Building Damage Magnitude from Satellite Images

Wheeler

Karimi

2020

Algorithms

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Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods.

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What is a remote sensing change detection technique? Towards a conceptual framework

Cited by 22 publications

References 24 publications

Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data

Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data

Inferring 3D change detection from bitemporal optical images

Deep Learning-Enabled Semantic Inference of Individual Building Damage Magnitude from Satellite Images

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