Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

Bueno, Inácio Thomaz; Júnior, Fausto Weimar Acerbi; Silveira, Eduarda Martiniano de Oliveira; Mello, José Márcio de; Carvalho, Luís Marcelo Tavares de; Gomide, Lucas Rezende; Withey, Kieran Daniel; Scolforo, José Roberto Soares

doi:10.3390/rs11050570

Cited by 21 publications

(17 citation statements)

References 58 publications

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“…where, λ denotes the relative importance for each of these four loss functions. Therefore, our main solutions are expressed as Equations (25) and (26).…”

Section: Contrastive Lossmentioning

confidence: 99%

Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images

2019

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As a fundamental and profound task in remote sensing, change detection from very-high-resolution (VHR) images plays a vital role in a wide range of applications and attracts considerable attention. Current methods generally focus on the research of simultaneously modeling and discriminating the changed and unchanged features. In practice, for bi-temporal VHR optical remote sensing images, the temporal spectral variability tends to exist in all bands throughout the entire paired images, making it difficult to distinguish none-changes and changes with a single model. In this paper, motivated by this observation, we propose a novel hybrid end-to-end framework named dual learning-based Siamese framework (DLSF) for change detection. The framework comprises two parallel streams which are dual learning-based domain transfer and Siamese-based change decision. The former stream is aimed at reducing the domain differences of two paired images and retaining the intrinsic information by translating them into each other’s domain. While the latter stream is aimed at learning a decision strategy to decide the changes in two domains, respectively. By training our proposed framework with certain change map references, this method learns a cross-domain translation in order to suppress the differences of unchanged regions and highlight the differences of changed regions in two domains, respectively, then focus on the detection of changed regions. To the best of our knowledge, the idea of incorporating dual learning framework and Siamese network for change detection is novel. The experimental results on two datasets and the comparison with other state-of-the-art methods verify the efficiency and superiority of our proposed DLSF.

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“…where, λ denotes the relative importance for each of these four loss functions. Therefore, our main solutions are expressed as Equations (25) and (26).…”

Section: Contrastive Lossmentioning

confidence: 99%

Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images

2019

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“…Change detection for deforestation is affected by seasonal variations in weather. In [111], the authors explicitly take SWIR-2 in Landsat to improve the change detection performance. In particular, object-based image analysis (OBIA) combined with a Random Forest (RF) is proposed to discriminate deforestation from seasonal changes.…”

Section: Supervisedmentioning

confidence: 99%

“…As shown, there are many diverse applications in the table. Cerrado (Brazilian savanna) biome monitoring [111] Object based approach Landsat…”

Section: Supervisedmentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

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Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.

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“…In both biomes, the deforested areas are predominantly converted to pasture [6,7], in addition to a strong expansion of soy in the Cerrado biome [8]. These biomes have different characteristics and accommodate rich biodiversity of endemic species, many of them vulnerable [9]. Therefore, the conservation of Amazon and Cerrado biomes is essential for the future of our planet.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes From Remote Sensing Imagery

et al. 2020

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Deforestation is one of the major threats to natural ecosystems. This process has a substantial contribution to climate change and biodiversity reduction. Therefore, the monitoring and early detection of deforestation is an essential process for preservation. Techniques based on satellite images are among the most attractive options for this application. However, many approaches involve some human intervention or are dependent on a manually selected threshold to identify regions that suffer deforestation. Motivated by this scenario, the present work evaluates Deep Learning-based strategies for automatic deforestation detection, namely, Early Fusion (EF), Siamese Network (SN), and Convolutional Support Vector Machine (CSVM) as well as Support Vector Machine (SVM), used as the baseline. The target areas are two regions with different deforestation patterns: the Amazon and Cerrado biomes in Brazil. The experiments used two co-registered Landsat 8 images acquired at different dates. The strategies based on Deep Learning achieved the best performance in our analysis in comparison with the baseline, with SN and EF superior to CSVM and SVM. In the same way, a reduction of the salt-and-pepper effect in the generated probabilistic change maps was noticed as the number of training samples increased. Finally, the work assesses how the methods can reduce the time invested in the visual inspection of deforested areas.

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Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

Cited by 21 publications

References 58 publications

Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images

Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes From Remote Sensing Imagery

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