Deforestation change detection using high-resolution multi-temporal X-Band SAR images and supervised learning classification

Barreto, Thiago L. M.; Rosa, Rafael A. S.; Wimmer, Christian; Nogueira, Joao B.; Almeida, Jurandy; Cappabianco, Fábio A. M.

doi:10.1109/igarss.2016.7730355

Cited by 13 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spatio-temporal character of our proposal may be understood as follows. Changes in space are associated with the temporal dynamics of the matrices D kl ) be the sequence of n squared deviations at location (k, l) and let d = (d (1) , . .…”

Section: Wavelet Energy Correlation Screeningmentioning

confidence: 99%

See 1 more Smart Citation

Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images

Fonseca

Negri²,

Pinheiro

et al. 2023

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

View full text Add to dashboard Cite

We introduce the WECS (Wavelet Energies Correlation Screening), an unsupervised method to detect spatio-temporal changes on multitemporal SAR images. The procedure is based on wavelet approximation for the multitemporal images, wavelet energy apportionment, and ultra-high dimensional correlation screening for the wavelet coefficients. We show WECS's performance on simulated multitemporal image data. We also evaluate the proposed method on a time series of 85 Sentinel-1 images of a forest region at the border of Brazil and French Guiana. Comparisons with well-known change detection methods found in the literature highlight the proposal's superiority in terms of change detection accuracy. Additionally, the introduced method has simple architecture and low computational cost.

show abstract

Section: Wavelet Energy Correlation Screeningmentioning

confidence: 99%

“…It allows practitioners to identify and evaluate modifications on land surfaces. Its relevance may be exemplified by changes due to human activity, such as deforestation [1], rapid urbanization [2] and glacier melting [3].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images

Fonseca

Negri²,

Pinheiro

et al. 2023

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

View full text Add to dashboard Cite

show abstract

“…Andreas et.al [28] monitor wetlands by adopting the curvelet-based change detection method in polarimetric SAR images. Thiago et al [29] adopted superpixel segmentation by combining Simple Linear Iterative Clustering (SLIC) with a Multi-layer Perceptron (MLP) to detect deforestation activities. A test statistic for equality of two complex Wishart matrices is derived in [27] for change detection of crops.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection in Complex Environment Using SAR Time Series

Kahar

2022

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

View full text Add to dashboard Cite

“…Remote sensing (RS) change detection is used to detect changes in a particular area on the surface of the Earth at different times. It provides references for urban planning [1], environment monitoring [2,3], and disaster assessment [4,5]. Several kinds of RS images exist-namely, optical RS images, electro-optical imaging images, and synthetic aperture radar (SAR) images.…”

Section: Introductionmentioning

confidence: 99%

Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

et al. 2021

View full text Add to dashboard Cite

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

show abstract

Deforestation change detection using high-resolution multi-temporal X-Band SAR images and supervised learning classification

Cited by 13 publications

References 13 publications

Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images

Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images

Ship Detection in Complex Environment Using SAR Time Series

Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

Contact Info

Product

Resources

About