Object-Based Image Analysis for Sago Palm Classification: The Most Important Features from High-Resolution Satellite Imagery

Hidayat, Sarip; Matsuoka, Masayuki; Baja, Sumbangan; Rampisela, Dorothea Agnes

doi:10.3390/rs10081319

Cited by 16 publications

(11 citation statements)

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“…Moreover, to properly assess the performance of the tested machine learning algorithms (CNN and RF), the significance of the overall accuracy between classifications was calculated using the McNemar statistical test [65,66]. Several studies have reported the use of the McNemar test to compare between two classification approaches [54,67,68]. In this test, a null hypothesis that there is no significant difference between OA values of the two compared classifications is proposed.…”

Section: Inter-comparison and Quality Assessmentmentioning

confidence: 99%

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

et al. 2019

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Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

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Section: Inter-comparison and Quality Assessmentmentioning

confidence: 99%

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

et al. 2019

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“…Single crown delineation accuracy could be reduced. In such a situation, one option would be to merge similar small objects [24] using spectral difference as a second step [80], although over-segmentation is generally preferred to under-segmentation [40,95]. For this assessment, no isolated tree crowns were used.…”

Section: Discussionmentioning

confidence: 99%

“…Spectral variables of reflectance values were calculated by pixel [6] and object-based methods using R [87], SAS software [88] and eCognition Developer. Object-based indices consist of a series of customized arithmetic features calculated using the mean of pixel values within an object of specific bands [16,19,80]. Arithmetic features were also calculated for the 95th percentile highest pixels within each object in order to use the brightest (sunlit) parts of each crown.…”

Section: Derived Variablesmentioning

confidence: 99%

“…The main objective of this study is to map 11 tree species using an object-based approach with WorldView-3 imagery and LiDAR data. Object-based image analysis brings the capacity to group homogenous pixels into meaningful objects based on their spectral values, which can then be analyzed by their shape, size, texture and contextual information [19], in contrary to the pixel-based approach [80]. We implemented modeling techniques in a global and hierarchical approach.…”

Section: Introductionmentioning

confidence: 99%

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Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

2020

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Species identification in Quebec, Canada, is usually performed with photo-interpretation at the stand level, and often results in a lack of precision which affects forest management. Very high spatial resolution imagery, such as WorldView-3 and Light Detection and Ranging have the potential to overcome this issue. The main objective of this study is to map 11 tree species at the tree level using an object-based approach. For modeling, 240 variables were derived from WorldView-3 with pixel-based and arithmetic feature calculation techniques. A global approach (11 species) was compared to a hierarchical approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were compared: support vector machine, classification and regression tree, random forest (RF), k-nearest neighbors, and linear discriminant analysis. Each model was assessed using 16-band or first 8-band derived variables, with the results indicating higher precision for the RF technique. Higher accuracies were found using 16-band instead of 8-band derived variables for the global approach (overall accuracy (OA): 75% vs. 71%, Kappa index of agreement (KIA): 0.72 vs. 0.67) and tree type level (OA: 99% vs. 97%, KIA: 0.97 vs. 0.95). For broadleaf individual species, higher accuracy was found using first 8-band derived variables (OA: 70% vs. 68%, KIA: 0.63 vs. 0.60). No distinction was found for individual conifer species (OA: 94%, KIA: 0.93). This paper demonstrates that a hierarchical classification approach gives better results for conifer species and that using an 8-band WorldView-3 instead of a 16-band is sufficient.

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“…Textural and geometric information are important data sources for describing spatial patterns and variations of surface features. Some previous studies demonstrated the usefulness of textural and geometric measures for wetland mapping [46][47][48]. In this paper, the gray-level co-occurrence matrix (GLCM) with window size 9 × 9 [49,50] and 64 grayscale quantization levels was used to generate the mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, standard deviation, and correlation features for GF-1 and ZY-3 images (Table 4).…”

Section: Calculation Of Spectral Indices and Textural Informationmentioning

confidence: 99%

An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data

Lou

et al. 2020

Remote Sensing

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Discriminating marsh vegetation is critical for the rapid assessment and management of wetlands. The study area, Honghe National Nature Reserve (HNNR), a typical freshwater wetland, is located in Northeast China. This study optimized the parameters (mtry and ntrees) of an object-based random forest (RF) algorithm to improve the applicability of marsh vegetation classification. Multidimensional datasets were used as the input variables for model training, then variable selection was performed on the variables to eliminate redundancy, which improved classification efficiency and overall accuracy. Finally, the performance of a new generation of Chinese high-spatial-resolution Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite images for marsh vegetation classification was evaluated using the improved object-based RF algorithm with accuracy assessment. The specific conclusions of this study are as follows: (1) Optimized object-based RF classifications consistently produced more than 70.26% overall accuracy for all scenarios of GF-1 and ZY-3 at the 95% confidence interval. The performance of ZY-3 imagery applied to marsh vegetation mapping is lower than that of GF-1 imagery due to the coarse spatial resolution. (2) Parameter optimization of the object-based RF algorithm effectively improved the stability and classification accuracy of the algorithm. After parameter adjustment, scenario 3 for GF-1 data had the highest classification accuracy of 84% (ZY-3 is 74.72%) at the 95% confidence interval. (3) The introduction of multidimensional datasets improved the overall accuracy of marsh vegetation mapping, but with many redundant variables. Using three variable selection algorithms to remove redundant variables from the multidimensional datasets effectively improved the classification efficiency and overall accuracy. The recursive feature elimination (RFE)-based variable selection algorithm had the best performance. (4) Optical spectral bands, spectral indices, mean value of green and NIR bands in textural information, DEM, TWI, compactness, max difference, and shape index are valuable variables for marsh vegetation mapping. (5) GF-1 and ZY-3 images had higher classification accuracy for forest, cropland, shrubs, and open water.

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Object-Based Image Analysis for Sago Palm Classification: The Most Important Features from High-Resolution Satellite Imagery

Cited by 16 publications

References 50 publications

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data

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