SAR Data for Land Use Land Cover Classification in a Tropical Region with Frequent Cloud Cover

Prudente, Victor Hugo Rohden; Sanches, I. D.; Adami, Marcos; Skakun, Sergii; Oldoni, Lucas Volochen; Xaud, Harón Abrahim Magalháes; Xaud, Maristela Ramalho; Zhang, Y.

doi:10.1109/igarss39084.2020.9323404

Cited by 8 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optical images are severely impacted by artifacts like clouds, fogs, and smokes. Clouds are frequent in some parts of the Earth, depending on latitude and local climate [11], [12], [13]. Furthermore, some incidents (e.g., volcanic eruptions, wildfires, wartime bombing) may themselves induce smoke, thus further reducing the chance of obtaining a clear optical image.…”

Section: Introductionmentioning

confidence: 99%

Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Saha

Shahzad

Ebel

et al. 2022

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

View full text Add to dashboard Cite

Change detection using satellite/aerial images is used to quantify the impacts of many natural and man-made disasters. At the occurrence of such events, both pre-change optical and Synthetic Aperture Radar (SAR) images can be obtained by going back in time. However, the availability of the post-change optical image is often hindered by the presence of artifacts like clouds.To circumnavigate this, we propose a novel change detection data setting which uses both optical and SAR images prechange, yet only SAR imagery post-change. For this challenging scenario, we propose a Siamese network that processes the pre-change and post-change SAR inputs using a shared set of weights, while the pre-change optical input is processed using a network that do not share the weights with the SAR inputs. The encoded weights from the three networks are fused and finally decoded using a common decoder to obtain the change map. Our model effectively fuses multi-sensor information and can obtain satisfactory result despite the absence of the post-change optical image. Experimental results on a multi-sensor urban dataset demonstrate the effectiveness of the proposed approach. Code is available at https:

show abstract

Section: Introductionmentioning

confidence: 99%

Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Saha

Shahzad

Ebel

et al. 2022

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

View full text Add to dashboard Cite

show abstract

“…We used two machine learning classifiers in this study: Random Forest (RF) and Multi-Layer Perception (MLP), using a python routine with the scikit-learn library (Pedregosa et al, 2011). After testing different parameters (Prudente et al, 2020b), we used numbers of trees equal to 30, no maximum depth of the tree, no maximum number of features and minimum split samples equal to 2 to the RF classifier, and one hidden layer with size equal to 50, rectified linear unit as activation function, stochastic gradient-based optimizer, alpha (L2 regularization) equal to 0.01, and learning rate values of 0.005 for the MLP classifier. The field data (polygons), section 2.1, were randomly separated into 75% for training and 25% to test which classification had the results that best fit the data collected in the field (testing data).…”

Section: Classification Methodsmentioning

confidence: 99%

Multisensor approach to land use and land cover mapping in Brazilian Amazon

Prudente

Skakun

Oldoni

et al. 2022

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

“…Using PALSAR and RF, [ 96 ] classified (overall accuracy = 72%) three species and coexisting land cover types recorded from 30 m x 30 m plots in a Subtropical savanna measuring 3 697 km 2 . [ 97 ] classified three woody plant species derived from plots measuring 10 m radius in a Tropical savanna region covering 224 300 km 2 . They applied RF and Multilayer Perceptron (MLP) classifiers to Sentinel-1 C-band image and found accuracies of 75% and 83%, respectively.…”

Section: Remote Sensing Of Savanna Woody Plant Species Diversity Usin...mentioning

confidence: 99%

Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods

2022

View full text Add to dashboard Cite

Despite savannas being known for their relatively sparse vegetation coverage compared to other vegetation ecosystems, they harbour functionally diverse vegetation forms. Savannas are affected by climate variability and anthropogenic factors, resulting in changes in woody plant species compositions. Monitoring woody plant species diversity is therefore important to inform sustainable biodiversity management. Remote sensing techniques are used as an alternative approach to labour-intensive field-based inventories, to assess savanna biodiversity. The aim of this paper is to review studies that applied remote sensing to assess woody plant species diversity in savanna environments. The paper first provides a brief account of the spatial distribution of savanna environments around the globe. Thereafter, it briefly defines categorical classification and continuous-scale species diversity assessment approaches for savanna woody plant estimation. The core review section divides previous remote sensing studies into categorical classification and continuous-scale assessment approaches. Within each division, optical, Radio Detection And Ranging (RADAR) and Light Detection and Ranging (LiDAR) remote sensing as applied to savanna woody species diversity is reviewed. This is followed by a discussion on multi-sensor applications to estimate woody plant species diversity in savanna. We recommend that future research efforts should focus strongly on routine application of optical, RADAR and LiDAR remote sensing of physiologically similar woody plant species in savannas, as well as on extending these methodological approaches to other vegetation environments.

show abstract

SAR Data for Land Use Land Cover Classification in a Tropical Region with Frequent Cloud Cover

Cited by 8 publications

References 13 publications

Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Multisensor approach to land use and land cover mapping in Brazilian Amazon

Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods

Contact Info

Product

Resources

About