Remote sensing tree classification with a multilayer perceptron

Sumsion, G Rex; Bradshaw, Michael S.; Hill, Kimball T; Pinto, Lucas D G; Piccolo, Stephen R.

doi:10.7717/peerj.6101

Cited by 20 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MLP classifiers performed best among the implemented classifiers because MLP mostly performed well for noisy, big, and complex data [35]. The MLP classifiers [36,37] are explained below; the production of input weight and bias are summed up using the summation function (ρ n ) given in Equation (29).…”

Section: Classificationmentioning

confidence: 99%

Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images

et al. 2020

View full text Add to dashboard Cite

The purpose of this research is to demonstrate the ability of machine-learning (ML) methods for liver cancer classification using a fused dataset of two-dimensional (2D) computed tomography (CT) scans and magnetic resonance imaging (MRI). Datasets of benign (hepatocellular adenoma, hemangioma, cyst) and malignant (hepatocellular carcinoma, hepatoblastoma, metastasis) liver cancer were acquired at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. The final dataset was generated by fusion of 1200 (100 × 6 × 2) MR and CT-scan images, 200 (100 MRI and 100 CT-scan) images size 512 × 512 for each class of cancer. The acquired dataset was preprocessed by employing the Gabor filters to reduce the noise and taking an automated region of interest (ROIs) using an Otsu thresholding-based segmentation approach. The preprocessed dataset was used to acquire 254 hybrid-feature data for each ROI, which is the combination of the histogram, wavelet, co-occurrence, and run-length features, while 10 optimized hybrid features were selected by employing (probability of error plus average correlation) feature selection technique. For classification, we deployed this optimized hybrid-feature dataset to four ML classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48, using a ten fold cross-validation method. MLP showed an overall accuracy of (95.78% on MRI and 97.44% on CT). Unfortunately, the obtained results were not promising, and there were some limitations due to the different modalities of the dataset. Thereafter, a fusion of MRI and CT-scan datasets generated the fused optimized hybrid-feature dataset. The MLP has shown a promising accuracy of 99% among all the deployed classifiers.

show abstract

Section: Classificationmentioning

confidence: 99%

Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Neural net classifiers were trained to extract pure grapevines row pixels from high-resolution hyperspectral images. We chose to use a neural net classifier due to the recent success of neural networks in hyperspectral image classification [76][77][78][79][80]. The neural net classifiers were implemented using ENVI version 5.5 software (Harris Geospatial Solutions Inc., Boulder, CO, USA) by providing regions of interest for five different classes, including grapevine, grass, vine canopy, shadow, and soil (Figure 3a).…”

Section: Canopy Row Extraction From Hyperspectral Imagesmentioning

confidence: 99%

Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology

et al. 2020

View full text Add to dashboard Cite

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs.

show abstract

“…Therefore, to balance the accuracy and time cost, three neurons for the hidden layer are selected in this experiment. As for the epochs and the learning rate, the figures 200 and 10 −4 are chosen, respectively, to prevent overfitting [34,51]. Furthermore, the activation function, loss function, and optimizer are selected as the rectified linear unit (ReLU), squared error, and adaptive moment estimation (ADAM), respectively.…”

Section: Phenological Normalization Based On Multilayer Perceptronmentioning

confidence: 99%

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

Seo

2019

Applied Sciences

View full text Add to dashboard Cite

Radiometric normalization is an essential preprocessing step that must be performed to detect changes in multi-temporal satellite images and, in general, relative radiometric normalization is utilized. However, most relative radiometric normalization methods assume a linear relationship and they cannot take into account nonlinear properties, such as the distribution of the earth's surface or phenological differences that are caused by the growth of vegetation. Thus, this paper proposes a novel method that assumes a nonlinear relationship and it uses a representative nonlinear regression model-multilayer perceptron (MLP). The proposed method performs radiometric resolution compression while considering both the complexity and time cost, and radiometric control set samples are extracted based on a no-change set method. Subsequently, the spectral index is selected for each band to compensate for the phenological properties, phenological normalization is performed based on MLP, and the global radiometric properties are adjusted through postprocessing. Finally, a performance evaluation is conducted by comparing the results herein with those from conventional relative radiometric normalization algorithms. The experimental results show that the proposed method outperforms conventional methods in terms of both visual inspection and quantitative evaluation. In other words, the applicability of the proposed method to the normalization of multi-temporal images with nonlinear properties is confirmed. Relative radiometric normalization, on the other hand, is an image-based normalization method that selects one image as a reference and matches the radiometric characteristics of a subject image [13,14]. That is, relative radiometric normalization is generally utilized since the multi-temporal images are normalized into common scales without extra parameters [12,15].Relative radiometric normalization can be classified into two categories: a global statistics-based method and a radiometric control set sample (RCSS)-based method. The former is performed by using statistical values of all pixels in the image, which includes histogram matching, minimum-maximum (MM), mean-standard deviation (MS), and simple regression (SR) [15,16]. In contrast, the latter is based on invariant pixels, known as RCSSs, which include dark set-bright set (DB), pseudo invariant features (PIF), and no-change sets (NC) [17][18][19][20]. At this time, except for histogram matching, normalization is performed through regression equations, while assuming a linear relationship between the pixels at the same position in each band [21]. However, most remote sensing data are nonlinearly distributed, and the surface of the earth is composed of a complex mixture of natural and man-made features that exhibit nonlinear characteristics [22][23][24]. Changes that are caused by vegetation in particular have the most typical characteristics, including nonlinearity, which induces serious disturbances when change detection is performed [25,26]. In addition, since optical sa...

show abstract

Remote sensing tree classification with a multilayer perceptron

Cited by 20 publications

References 40 publications

Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images

Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images

Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

Contact Info

Product

Resources

About