Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery

Mahdianpari, Masoud; Salehi, Bahram; Mohammadimanesh, Fariba; Motagh, Mahdi

doi:10.1016/j.isprsjprs.2017.05.010

Cited by 247 publications

(169 citation statements)

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“…Novack et al [54] showed that RF classifier can evaluate each attribute internally; thus, it is less sensitive to the increase of variables (Tables 5 and 6). The object-based classifier can provide faster and better results and can be easily applied to classify forest types [24,39,40,55]. In addition, this classification method has the ability to handle predictor variables with a multimodal distribution well due to the high variability in time and space [50,56]; especially, no sophisticated parameter tuning is required.…”

Section: Discussionmentioning

confidence: 99%

“…Each decision tree gives a classification result for the samples not chosen as training samples. The decision tree "votes" for that class, and the final class is determined by the largest number of votes [24]. This approach can handle thousands of input variables without variable deletion and is not susceptible to over-fitting as the anti-noising ability is enhanced by randomization.…”

Section: Object-based Random Forestmentioning

confidence: 99%

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Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

et al. 2018

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Abstract:Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales.

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Section: Discussionmentioning

confidence: 99%

Section: Object-based Random Forestmentioning

confidence: 99%

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

et al. 2018

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“…In particular, they are of great use when the efficiency of optical sensors is hampered by cloud cover and day/night conditions. Furthermore, SAR signal penetration depth through vegetation and soil offers additional information unavailable from optical remote sensing data [44,45]. This is of great importance for monitoring the flooding status of vegetation due to enhanced double bounce scattering effects.…”

Section: Wetland Classification Using Sar Datamentioning

confidence: 99%

“…Accordingly, several studies reported the superior capability of L-band relative to the shorter wavelengths (e.g., C-and X-band) for monitoring woody wetlands (e.g., swamp), since the incident SAR signal interacts with larger trunk and branch components [48,49]. In particular, L-band holds great promise in discriminating between forested wetland (e.g., swamp) and dry forest [45,50]. However, shorter wavelengths are preferred for monitoring herbaceous vegetation because SAR wavelength and vegetation canopies (e.g., leaf) are relatively the same size [51].…”

Section: Sar Wavelengthmentioning

confidence: 99%

A Collection of Novel Algorithms for Wetland Classification with SAR and Optical Data

Salehi¹,

Mahdianpari²,

Amani³

et al. 2019

Wetlands Management - Assessing Risk and Sustainable Solutions

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Wetlands are valuable natural resources that provide many benefits to the environment, and thus, mapping wetlands is crucially important. We have developed land cover and wetland classification algorithms that have general applicability to different geographical locations. We also want a high level of classification accuracy (i.e., more than 90%). Over that past 2 years, we have been developing an operational wetland classification approach aimed at a Newfoundland/Labrador province-wide wetland inventory. We have developed and published several algorithms to classify wetlands using multi-source data (i.e., polarimetric SAR and multi-spectral optical imagery), object-based image analysis, and advanced machine-learning tools. The algorithms have been tested and verified on many large pilot sites across the province and provided overall and class-based accuracies of about 90%. The developed methods have general applicability to other Canadian provinces (with field validation data) allowing the creation of a nation-wide wetland inventory system.

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“…In a lot of studies, RF classifier performed equally well to SVMs in terms of classification accuracy and training time [57][58][59], but RF was acknowledged more user-friendly for the number of user-defined parameters required by RF classifiers is less than the number required for SVMs and easier to define [60]. The RF algorithm has not only been applied successfully in pixel-based image analyses, but has also shown great promise in the OBIA method for its high accuracy [61] and robustness to training sample reduction and feature selection [62,63]. Therefore, the RF algorithm was adopted in the current work.…”

Section: Recognizing Changed Objects Using the Rf Algorithmmentioning

confidence: 99%

Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

Zhou

et al. 2018

IJGI

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Despite increases in the spatial resolution of satellite imagery prompting interest in object-based image analysis, few studies have used object-based methods for monitoring changes in coral reefs. This study proposes a high accuracy object-based change detection (OBCD) method intended for coral reef environment, which uses QuickBird and WorldView-2 images. The proposed methodological framework includes image fusion, multi-temporal image segmentation, image differencing, random forests models, and object-area-based accuracy assessment. For validation, we applied the method to images of four coral reef study sites in the South China Sea. We compared the proposed OBCD method with a conventional pixel-based change detection (PBCD) method by implementing both methods under the same conditions. The average overall accuracy of OBCD exceeded 90%, which was approximately 20% higher than PBCD. The OBCD method was free from salt-and-pepper effects and was less prone to images misregistration in terms of change detection accuracy and mapping results. The object-area-based accuracy assessment reached a higher overall accuracy and per-class accuracy than the object-number-based and pixel-number-based accuracy assessment. methods for coral reef change detection. However, because of the limitations of medium-resolution sensors such as Landsat and SPOT, it is difficult to distinguish coral reef geomorphological dynamics from sea level rise [10], and to detect changes on the scale of a few meters in coral reef habitats [11]. With continued refinement of the spatial resolution of satellite imagery, conventional per-pixel methods have been found susceptible to a number of challenges in relation to change detection, including image misregistration [6,12] and salt-and-pepper effects [13,14]. Coral reef images, lack of distinct and stable texture features, are difficult to be accurately registered to each other [15], which makes the traditional pixel-based approach less promising in coral reef change detection using high-resolution images.Recent years have seen an increase in the number of studies using object-based image analysis (OBIA) [13]. OBIA has also been applied in coral reef environment, from geomorphological mapping to benthic community discrimination [16][17][18]. OBIA represents an effective combination of both the contextual analysis of visual interpretation and the quantitative analysis of the pixel-based method [19]. It has been proven that image registration error greatly affects the per-pixel change detection accuracy while the object-based method is less sensitive to image misregistration [20,21]. However, to the best of our knowledge, few studies have used object-based change detection (OBCD) methods in coral reef change detection. Generally, there are two possible strategies for OBCD methods: post-classification comparison and multi-temporal image object analysis [22]. The essence of the post-classification comparison approach lies in the initial classification, i.e., images acquired at different times are classifi...

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Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery

Cited by 247 publications

References 70 publications

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

A Collection of Novel Algorithms for Wetland Classification with SAR and Optical Data

Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

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