Tree Species Extraction and Land Use/Cover Classification From High-Resolution Digital Orthophoto Maps

Jamıl, Akhtar; Bayram, Bülent

doi:10.1109/jstars.2017.2756864

Cited by 19 publications

(13 citation statements)

References 24 publications

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“…Machine learning (ML) approaches have proven to be very effective for classification of remote sensing (RS) data by extracting and selection of feature for hyperspectral images [19], [20]. ML methods have produced significantly promising results in many RS applications, such as tree delineation [21], land cover classification [22], buildings and tree species extraction [23], [24], fault diagnosis [25] and fault-tolerant control [26]. In the context of glaciers, ML techniques have also been used for mapping on large glaciers from RS data.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Algorithms for Mapping Debris-Covered Glaciers: The Hunza Basin Case Study

et al. 2020

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Global warming is one of the main challenges of recent times. The glaciers are melting faster than expected which has resulted in global mean sea level rise and increased the risk of floods. The development of modern remote sensing technology has made it possible to obtain images more frequently than ever before. On the other side, the availability of high-performance computing hardware and processing techniques have made it possible to provide a cost-effective solution to monitor the temporal changes of glaciers at a large scale. In this study, supervised machine learning methods are investigated for automatic classification of glacier covers from multi-temporal Sentinel-2 imagery using texture, topographic, and spectral data. Three most commonly used supervised machine learning techniques were investigated: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The proposed method was employed on the data obtained from Passu watershed in Hunza Basin located along the Hunza river in Pakistan. Three main classes were considered: glaciers, debris-covered glaciers and non-glaciated areas. The data was split into training (70%) and testing datasets (30%). Finally, an area-based accuracy assessment was performed by comparing the results obtained for each classifier with the reference data. Experiments showed that the results produced for all classifiers were highly accurate and visually more consistent with the depiction of glacier cover types. For all experiments, random forest performed the best (Kappa = 0.95, f-measure = 95.06%) on all three classes compared to ANN (Kappa = 0.92, f-measure = 92.05) and SVM (Kappa = 0.89, f-measure = 91.86% on average). The high classification accuracy obtained to distinguished debris-covered glaciers using our approach will be useful to determine the actual available water resources which can be further helpful for hazard and water resource management.INDEX TERMS Artificial neural network (ANN), glacier mapping and remote sensing, random forest (RF), support vector machine (SVM).

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Section: Introductionmentioning

confidence: 99%

Machine-Learning Algorithms for Mapping Debris-Covered Glaciers: The Hunza Basin Case Study

et al. 2020

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“…The fully connected (FC) layers follow the abovementioned layers, mapping the distributed feature representation to the sample label space, and the last FC layer, which is called softmax layer, computes values for each class. The convolution operation process with BN operation is shown in equation (1).…”

Section: Cnnmentioning

confidence: 99%

“…where I denotes a matrix, K (1) and K (2) denotes two 2D kernels with compatible sizes, and ⊕ is the element-wise addition of the kernel parameters on the corresponding positions.…”

Section: Asymmetric Convolution Blockmentioning

confidence: 99%

“…Forest is an important part of the earth ecosystem [1], and the identification of forest tree species is of great significance to the sustainable management and utilization of forest ecosystems [2]. The composition and distribution of forest tree species are closely related to forest biomass, biodiversity, forest quality and other factors [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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ACTL: Asymmetric Convolutional Transfer Learning for Tree Species Identification Based on Deep Neural Network

Shi

et al. 2021

IEEE Access

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The identification of tree species is of great significance to the sustainable management and utilization of forest ecosystems. Hyperspectral data provide sufficient spectral and spatial information to classify tree species. Convolutional neural networks (CNN) have achieved great success in hyperspectral image (HSI) classification. The outstanding performance of CNN in HSI classification relies on sufficient training samples. However, it's expensive and time consuming to acquire labeled training samples. In this article, a novel asymmetric convolutional transfer learning model for HSI classification is proposed. First, the tree species identification dataset is built from Goddard's LiDAR, Hyperspectral & Thermal (G-LiHT) data. Then, the asymmetric convolutional transfer learning model and weights trained on ImageNet dataset are used to initialize the weights of the HSI classification model. Finally, a well fine-tuned neural network on tree species dataset is used to perform the HSI classification task. The experimental results reveal that the proposed model with asymmetric convolutional blocks effectively improves the accuracy of Howland forest tree species identification and provides a new idea for the classification of hyperspectral remote sensing images.INDEX TERMS Deep neural networks, G-LiHT, hyperspectral image, asymmetric convolution block, transfer learning.

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“…The SVM algorithm used for objectbased classification produced more successful results than the maximum likelihood algorithm used for pixel-based classification (Overall accuracy SVM: 85.99%, ML: 75.83%). Similarly, in [4] supervised learning algorithms SVM, artificial neural networks (ANN) and random forests were used for classification of ground cover (tea trees, other trees, bare areas, impermeable surfaces). Accuracy rates for tea trees were 87% for SVM, 89% for YSA and 86% for RF.…”

Section: Literature Reviewmentioning

confidence: 99%

Land Cover Classification using Machine Learning Approaches from High Resolution Images

Jamıl

Khan

Hameed

et al. 2021

Journal of Applied and Emerging Sciences

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Land cover classification has become an interesting research area in the field of remote sensing. Machine learning techniques have shown great success for various application in the domain of land cover classification. This paper focuses on the classification of land covers obtained from high resolution images using two well-known classification methods by integrating with object-based segmentation technique. First, graph-based minimal spanning tree segmentation was applied to segment the original image pixels into objects. The segmented objects were then used to obtained spectral, spatial and texture features which were then combined to form a single high dimensional feature vector. These features were then used to train and test the artificial neural network (ANN) and support vector machine (SVM). The proposed method was evaluated on a dataset consisting of high resolution multi-spectral images with four classes (tea area, other trees, roads and builds, bare land). The experiments showed that ANN was more accuracy as it scored average accuracy of 82.60% while SVM produced 73.66%. Moreover, when postprocessing using majority analysis was applied, the average accuracy improved to 86.18%.

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Tree Species Extraction and Land Use/Cover Classification From High-Resolution Digital Orthophoto Maps

Cited by 19 publications

References 24 publications

Machine-Learning Algorithms for Mapping Debris-Covered Glaciers: The Hunza Basin Case Study

Machine-Learning Algorithms for Mapping Debris-Covered Glaciers: The Hunza Basin Case Study

ACTL: Asymmetric Convolutional Transfer Learning for Tree Species Identification Based on Deep Neural Network

Land Cover Classification using Machine Learning Approaches from High Resolution Images

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