One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

Guidici, Daniel; Clark, Matthew L.

doi:10.3390/rs9060629

Cited by 86 publications

(74 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classification of the land cover was performed using a CNN [14,22,[31][32][33][34][35]. CNNs simulate the working of a human brain using a multilayer structure [26].…”

Section: Methodsmentioning

confidence: 99%

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

et al. 2019

View full text Add to dashboard Cite

In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)-digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense.

show abstract

“…The classification of the land cover was performed using a CNN [14,22,[31][32][33][34][35]. CNNs simulate the working of a human brain using a multilayer structure [26].…”

Section: Methodsmentioning

confidence: 99%

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The second new feature type explored uses a 2-D matrix approach. Different to the 1-D vector approach [32], the 2-D matrices are aligned with spectral bands (x axis) and time (y axis) as shown in Figure 1b. The pixel value of the first spectral band at (m, n) for the t th period is designated as pm,n,(t-1)×N+1.…”

Section: -D Feature Extractionmentioning

confidence: 99%

“…CNNs have also integrated with other algorithms, such as multilayer perceptrons [30] and support vector machines [31]. Many studies have reported that CNNs have contributed to an accuracy improvement of land cover classification, with the overall accuracy ranging from 81% to 93%, depending on the sensor type, spatial resolution of input images, and target classes [18,19,21,27,[29][30][31][32][33].Feature engineering is defined as the process of transforming raw data into features for better representation of the given problem, which can result in an improvement of the model accuracy on unseen data [34]. Good features are a contributing factor in model performance since machine learning algorithms are problem specific and dependent on their domains.…”

mentioning

confidence: 99%

“…First, two different input features-a new representation of spectral information based on the framework of Kim et al [35] and a 2-D matrix approach-were applied on CNNs with multi-temporal multispectral images. Second, we compared the performance of our proposed models with the line graph image-based CNN model [35], patch-based CNN, the 1-D CNN model [32], random forest (RF), and support vector machine (SVM). Third, we analyzed the effect of sample size on the models through oversampling and undersampling.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

et al. 2020

View full text Add to dashboard Cite

This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of this study are that (1) the shape of the converted 2-D images is more intuitive for human eyes to interpret when compared to 1-D spectral input; and (2) CNNs are highly specialized and may be able to similarly utilize this information for land cover classification. Four seasonal Landsat 8 images over three study areas—Lake Tapps, Washington, Concord, New Hampshire, USA, and Gwangju, Korea—were used to evaluate the proposed approach for nine land cover classes compared to several other methods: Random forest (RF), support vector machine (SVM), 1-D CNN, and patch-based CNN. Oversampling and undersampling approaches were conducted to examine the effect of the sample size on the model performance. The CNN-Polygon had better performance than the other methods, with overall accuracies of about 93%–95 % for both Concord and Lake Tapps and 80%–84% for Gwangju. The CNN-Polygon particularly performed well when the training sample size was small, less than 200 per class, while the CNN-Matrix resulted in similar or higher performance as sample sizes became larger. The contributing input variables to the models were carefully analyzed through sensitivity analysis based on occlusion maps and accuracy decreases. Our result showed that a more visually intuitive representation of input features for CNN-based classification models yielded higher performance, especially when the training sample size was small. This implies that the proposed graph-based CNNs would be useful for land cover classification where reference data are limited.

show abstract

“…For example, W. Hu et al [13] trained a simple one-dimensional (1D) five-layer CNN that directly classifies hyperspectral images in spectral domain. D. Guidici et al [14] attempted to carry out 1D CNN for classifying land cover from multi-seasonal hyperspectral imagery, followed by the features extracted from the spectral domain through the training of the network. To avoid overfitting, S. Mei et al [15] suggested a spectral-spatial-featurebased classification framework, which jointly makes use of batch normalization, dropout, and parametric rectified linear 2 Mathematical Problems in Engineering unit activation function and a 1D CNN.…”

Section: Introductionmentioning

confidence: 99%

FMRSS Net: Fast Matrix Representation-Based Spectral-Spatial Feature Learning Convolutional Neural Network for Hyperspectral Image Classification

Hou

Lei

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Convolutional Neural Network-(CNN-) based land cover classification algorithms have recently been applied in hyperspectral images (HSI) field. However, the large-scale training parameters bring huge computation burden to CNN and the spatial variability of spectral signatures leads to relative low classification accuracy. In this paper, we propose a CNN-based classification framework that extracts square matrix representation-based spectral-spatial features and performs land cover classification. Numerical results on popular datasets show that our framework outperforms sparsity-based approaches like basic thresholding classifier-weighted least squares (BTC-WLS) and other deep learning-based methods in terms of both classification accuracy and computational cost.

show abstract

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

Cited by 86 publications

References 28 publications

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

FMRSS Net: Fast Matrix Representation-Based Spectral-Spatial Feature Learning Convolutional Neural Network for Hyperspectral Image Classification

Contact Info

Product

Resources

About