Mapping invasive vegetation using AISA Eagle airborne hyperspectral imagery in the Mid-Ipoly-Valley

Burai, Péter; Láposi, Réka; Enyedi, Péter; Schmotzer, András; Bognar, Veronika Kozma

doi:10.1109/whispers.2011.6080947

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…Solidago spp. has also been classified with high accuracy (F1 about 0.83, UA = 0.71, PA = 1.0) on the Hungarian-Slovak cross-border site using 15 MNF bands (a mosaic of hyperspectral data from AISA Eagle II) and the maximum likelihood method [61]. High identification accuracy of one of the goldenrod species, Solidago altissima (F1 score of about 0.86, UA = 0.94, PA = 0.80), was also obtained during the research conducted in Watarase wetlands in Japan with the help of only 3 MNF transformation bands (a mosaic of hyperspectral data from AISA Eagle) and generalized linear models [19].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

2020

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Invasive and expansive plant species are considered a threat to natural biodiversity because of their high adaptability and low habitat requirements. Species investigated in this research, including Solidago spp., Calamagrostis epigejos, and Rubus spp., are successfully displacing native vegetation and claiming new areas, which in turn severely decreases natural ecosystem richness, as they rapidly encroach on protected areas (e.g., Natura 2000 habitats). Because of the damage caused, the European Union (EU) has committed all its member countries to monitor biodiversity. In this paper we compared two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to identify Solidago spp., Calamagrostis epigejos, and Rubus spp. on HySpex hyperspectral aerial images. SVM and RF are reliable and well-known classifiers that achieve satisfactory results in the literature. Data sets containing 30, 50, 100, 200, and 300 pixels per class in the training data set were used to train SVM and RF classifiers. The classifications were performed on 430-spectral bands and on the most informative 30 bands extracted using the Minimum Noise Fraction (MNF) transformation. As a result, maps of the spatial distribution of analyzed species were achieved; high accuracies were observed for all data sets and classifiers (an average F1 score above 0.78). The highest accuracies were obtained using 30 MNF bands and 300 sample pixels per class in the training data set (average F1 score > 0.9). Lower training data set sample sizes resulted in decreased average F1 scores, up to 13 percentage points in the case of 30-pixel samples per class.

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Section: Discussionmentioning

confidence: 99%

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

2020

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“…Three supervised classification methods (MLC, RF and SVM) were selected as classifiers, since their efficiency was proven in vegetation mapping in recent studies [34,35].…”

Section: Applied Classification Methodsmentioning

confidence: 99%

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

et al. 2015

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Abstract:Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data in this complex situation. To reach the highest classification accuracy, we tested traditional image classifiers (maximum likelihood classifier-MLC), machine learning algorithms (support vector machine-SVM, random forest-RF) and feature extraction (minimum noise fraction (MNF)-transformation) on training datasets of different sizes. Digital images were acquired from an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400-1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. For the classification, we established twenty vegetation classes based on the dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset with various training sample sizes between 10 and 30 pixels. In order to select the optimal number of the transformed features, we applied SVM, RF and MLC classification to 2-15 MNF transformed bands. In the case of the original bands, SVM and RF classifiers provided high accuracy irrespective of the number of the training pixels. We found that SVM and RF produced the best accuracy when using the first nine MNF transformed bands; involving further features did not increase classification accuracy. SVM and RF provided high accuracies with the transformed bands, especially in the case of the OPEN ACCESS Remote Sens. 2015, 7 2047 aggregated groups. Even MLC provided high accuracy with 30 training pixels (80.78%), but the use of a smaller training dataset (10 training pixels) significantly reduced the accuracy of classification (52.56%). Our results suggest that in alkali landscapes, the application of SVM is a feasible solution, as it provided the highest accuracies compared to RF and MLC. SVM was not sensitive in the training sample size, which makes it an adequate tool when only a limited number of training pixels are available for some classes.

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“…To increase efficiency in sampling, while maintaining or enhancing accuracy, airborne remote sensing of milkweed plants has been proposed by Burai et al. (), consistent with advances in using low‐altitude drones to survey for other beneficial plants (Cruzan et al. ) and weed species (Barrero et al.…”

Section: Introductionmentioning

confidence: 95%

“…Burai et al. () proposed milkweed plant mapping using airborne hyperspectral imagery. They were able to classify various milkweed plant locations using the spectral angle mapper (SAM) method.…”

Section: Introductionmentioning

confidence: 99%

Milkweed (Asclepias syriaca) plant detection using mobile cameras

et al. 2020

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2020. Milkweed (Asclepias syriaca) plant detection using mobile cameras. Ecosphere 11(1):Abstract. Milkweed (Asclepias spp.) are host plants of monarch butterflies (Danaus plexippus). It is important to detect milkweed plant locations to assess the status and trends of monarch habitat in support of monarch conservation programs. In this paper, we describe autonomous detection of milkweed plants using cameras mounted to vehicles. For detection, we used both aggregated channel features (ACF) for running the detectors on embedded computing platforms with central processing unit and faster regionbased convolutional neural network (Faster R-CNN) with a ResNet architecture-based detector that is suitable for graphics processing unit optimized processing. The ACF-based model produced 0.89 mean average precision (mAP) on the training dataset and 0.29 mAP on the test dataset, whereas the ResNet-based Faster R-CNN model provided 0.98 mAP on training and 0.44 mAP on the test dataset. The detections were used to calculate approximate densities of milkweed plants in geo-referenced locations based on global positioning system point correspondences of recorded images. Probability-of-count distributions are compared for the actual milkweed plant locations near roadsides. This is one of the first examples of using automated milkweed plant detection and density mapping using a vehicle-mounted camera.

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Mapping invasive vegetation using AISA Eagle airborne hyperspectral imagery in the Mid-Ipoly-Valley

Cited by 7 publications

References 16 publications

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

Milkweed (Asclepias syriaca) plant detection using mobile cameras

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