A New Convolutional Kernel Classifier for Hyperspectral Image Classification

Ansari, Mohsen; Homayouni, Saeid; Safari, Abdolreza; Niazmardi, Saeid

doi:10.1109/jstars.2021.3123087

Cited by 17 publications

(8 citation statements)

References 39 publications

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“…5a). The second model is a residual neural network (ResNet) utilizing 1-D convolutions instead of the standard 2-D. Convolutional neural networks (CNNs) using 1-D convolutions are often applied in hyperspectral classification problems (Ansari et al, 2021) where modeling spectral content, rather than spatial content, is the aim. We then test a 1-D ResNet (Hannun et al, 2019) for modeling the S1/S2 data (Fig.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing

Ahlswede

Schulz²,

Gava³

et al. 2023

Earth Syst. Sci. Data

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Abstract. Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation for individual species determination is labor-intensive. Hence, various studies focusing on forests have investigated the benefits of multiple sensors for automated tree species classification. However, transferable deep learning approaches for large-scale applications are still lacking. This gap motivated us to create a novel dataset for tree species classification in central Europe based on multi-sensor data from aerial, Sentinel-1 and Sentinel-2 imagery. In this paper, we introduce the TreeSatAI Benchmark Archive, which contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. We propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data. Finally, we provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods. We found that residual neural networks (ResNet) perform sufficiently well with weighted precision scores up to 79 % only by using the RGB bands of aerial imagery. This result indicates that the spatial content present within the 0.2 m resolution data is very informative for tree species classification. With the incorporation of Sentinel-1 and Sentinel-2 imagery, performance improved marginally. However, the sole use of Sentinel-2 still allows for weighted precision scores of up to 74 % using either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since the dataset is derived from real-world reference data, it contains high class imbalances. We found that this dataset attribute negatively affects the models' performances for many of the underrepresented classes (i.e., scarce tree species). However, the class-wise precision of the best-performing late fusion model still reached values ranging from 54 % (Acer) to 88 % (Pinus). Based on our results, we conclude that deep learning techniques using aerial imagery could considerably support forestry administration in the provision of large-scale tree species maps at a very high resolution to plan for challenges driven by global environmental change. The original dataset used in this paper is shared via Zenodo (https://doi.org/10.5281/zenodo.6598390, Schulz et al., 2022). For citation of the dataset, we refer to this article.

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Section: Deep Learning Modelsmentioning

confidence: 99%

TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing

Ahlswede

Schulz²,

Gava³

et al. 2023

Earth Syst. Sci. Data

View full text Add to dashboard Cite

show abstract

“…where 𝑓= activation function 𝑥 = input data from layer l-1 𝑤 = weight parameter 𝑏 = bias vector Equation 2 can be used to calculate the output of the j th feature map (z) in the i th layer at the spatial position of (x,y) in the 2D convolution layer (Ansari et al, 2021;.…”

Section: Convolution Layermentioning

confidence: 99%

Oil Spill Detection in the Caspian Sea With a Sar Image Using a Densenet Model

Barzegar

Seydi

Farzaneh

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Hydrocarbon spills play a vital part in contaminating water resources such as the seas and oceans. Establishing a quick and accurate approach to identifying hydrocarbons is critical to addressing these pollutants' harmful effects on the aquatic environment and local residents. The use of satellite remote sensing to detect large-scale oil spills is one of the most extensively used domains for this purpose. A machine learning method with high speed and accuracy was proposed in this study to detect occurring hydrocarbon leaks in a SAR image captured by the Sentinel-1A satellite in the Caspian Sea. The suggested method is a dense-structured network of deep learning (DenseNet) that takes the required image as input and divides it into two classes, namely, oil Spill and non-oil Spill. This dense Network outperforms the standard convolutional neural network algorithm (CNN). As a result of using the Sentinel-1A SAR image of the Caspian Sea, we achieved an overall accuracy of 99.83% and a Kappa coefficient of 0.9055.

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“…5A). The second model is a residual neural network (ResNet) utilizing 1-D convolutions instead of the standard 2-D. Convolutional neural networks (CNNs) using 1-D convolutions are often applied in hyperspectral classification problems [Ansari et al, 2021] where modelling spectral content, rather than spatial content, is the aim. We then test a 1-D ResNet [Hannun et al, 2019] for modelling the S1/S2 data (Fig.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

TreeSatAI Benchmark Archive: A multi-sensor, multi-label dataset for tree species classification in remote sensing

Ahlswede¹,

Schulz²,

Gava

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation for individual species determination is labour-intensive. Hence, various studies focusing on forests have investigated the benefits of multiple sensors for automated tree species classification. However, transferable deep learning approaches for large-scale applications are still lacking. This gap motivated us to create a novel dataset for tree species classification in Central Europe based on multi-sensor data from aerial, Sentinel-1 and Sentinel-2 imagery. In this paper, we introduce the TreeSatAI Benchmark Archive, which contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. We propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data. Finally, we provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods. We found that residual neural networks (ResNet) perform sufficiently well with weighted precision scores up to 79 % only by using the RGB bands of aerial imagery. This result indicates that the spatial content present within the 0.2 m resolution data is very informative for tree species classification. With the incorporation of Sentinel-1 and Sentinel-2 imagery, performance improved marginally. However, the sole use of Sentinel-2 still allows for weighted precision scores of up to 74 % using either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since the dataset is derived from real-world reference data, it contains high class imbalances. We found that this dataset attribute negatively affects the models' performances for many of the underrepresented classes (i.e., scarce tree species). However, the class-wise precision of the best performing late fusion model still reached values ranging from 54 % (Acer) to 88 % (Pinus). Based on our results, we conclude that deep learning techniques using aerial imagery could considerably support forestry administration in the provision of large-scale tree species maps at a very high resolution to plan for challenges driven by global environmental change. The original dataset used in this paper is shared via Zenodo (https://doi.org/10.5281/zenodo.6598390) [Schulz et al., 2022]. For citation of the dataset, we refer to this article.

show abstract

A New Convolutional Kernel Classifier for Hyperspectral Image Classification

Cited by 17 publications

References 39 publications

TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing

TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing

Oil Spill Detection in the Caspian Sea With a Sar Image Using a Densenet Model

TreeSatAI Benchmark Archive: A multi-sensor, multi-label dataset for tree species classification in remote sensing

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