Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

He, Yongtao; Jia, Kebin; Wei, Zhihao

doi:10.3390/rs15092412

Cited by 9 publications

(2 citation statements)

References 57 publications

(73 reference statements)

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“…Therefore it is of paramount importance to continuously monitor forests in order to understand the changes that occur with respect to time. Surveys were used to conduct forest monitoring, but such a technique is costly and cannot be completed in a short period of time [ 4 ]. With the development of advanced modern sensors, remote sensing has made it possible to monitor land cover on a large scale.…”

Section: Introductionmentioning

confidence: 99%

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation

Kwenda,

Gwetu,

Fonou-Dombeu

2024

J. Imaging

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Forests play a pivotal role in mitigating climate change as well as contributing to the socio-economic activities of many countries. Therefore, it is of paramount importance to monitor forest cover. Traditional machine learning classifiers for segmenting images lack the ability to extract features such as the spatial relationship between pixels and texture, resulting in subpar segmentation results when used alone. To address this limitation, this study proposed a novel hybrid approach that combines deep neural networks and machine learning algorithms to segment an aerial satellite image into forest and non-forest regions. Aerial satellite forest image features were first extracted by two deep neural network models, namely, VGG16 and ResNet50. The resulting features are subsequently used by five machine learning classifiers including Random Forest (RF), Linear Support Vector Machines (LSVM), k-nearest neighbor (kNN), Linear Discriminant Analysis (LDA), and Gaussian Naive Bayes (GNB) to perform the final segmentation. The aerial satellite forest images were obtained from a deep globe challenge dataset. The performance of the proposed model was evaluated using metrics such as Accuracy, Jaccard score index, and Root Mean Square Error (RMSE). The experimental results revealed that the RF model achieved the best segmentation results with accuracy, Jaccard score, and RMSE of 94%, 0.913 and 0.245, respectively; followed by LSVM with accuracy, Jaccard score and RMSE of 89%, 0.876, 0.332, respectively. The LDA took the third position with accuracy, Jaccard score, and RMSE of 88%, 0.834, and 0.351, respectively, followed by GNB with accuracy, Jaccard score, and RMSE of 88%, 0.837, and 0.353, respectively. The kNN occupied the last position with accuracy, Jaccard score, and RMSE of 83%, 0.790, and 0.408, respectively. The experimental results also revealed that the proposed model has significantly improved the performance of the RF, LSVM, LDA, GNB and kNN models, compared to their performance when used to segment the images alone. Furthermore, the results showed that the proposed model outperformed other models from related studies, thereby, attesting its superior segmentation capability.

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Section: Introductionmentioning

confidence: 99%

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation

Kwenda,

Gwetu,

Fonou-Dombeu

2024

J. Imaging

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“…Continuous monitoring of forest cover is therefore particularly essential. Forest cover monitoring can quantify important information such as forest area, cover, deforestation, and fire incidence, and provide a reliable basis for ecological monitoring [3]. At the same time, it is possible to understand the current situation and development trends of the forest ecological environment and to provide basic data for measures to protect it.…”

Section: Introductionmentioning

confidence: 99%

Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction

Xia,

Yang,

Zhang

et al. 2023

Forests

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Sentinel-2 serves as a crucial data source for monitoring forest cover change. In this study, a sub-pixel mapping of forest cover is performed on Sentinel-2 images, downscaling the spatial resolution of the positioned results to 2.5 m, enabling sub-pixel-level forest cover monitoring. A novel sub-pixel mapping with edge-matching correction is proposed on the basis of the Sentinel-2 images, combining edge-matching technology to extract the forest boundary of Jilin-1 images at sub-meter level as spatial constraint information for sub-pixel mapping. This approach enables accurate mapping of forest cover, surpassing traditional pixel-level monitoring in terms of accuracy and robustness. The corrected mapping method allows more spatial detail to be restored at forest boundaries, monitoring forest changes at a smaller scale, which is highly similar to actual forest boundaries on the surface. The overall accuracy of the modified sub-pixel mapping method reaches 93.15%, an improvement of 1.96% over the conventional Sub-pixel-pixel Spatial Attraction Model (SPSAM). Additionally, the kappa coefficient improved by 0.15 to reach 0.892 during the correction. In summary, this study introduces a new method of forest cover monitoring, enhancing the accuracy and efficiency of acquiring forest resource information. This approach provides a fresh perspective in the field of forest cover monitoring, especially for monitoring small deforestation and forest degradation activities.

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Automatic Identification of Forest Areas in the “Carolina” Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study

Guapaz,

Jervis,

Haro

et al. 2024

Communications in Computer and Information Science

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Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

Cited by 9 publications

References 57 publications

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation

Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction

Automatic Identification of Forest Areas in the “Carolina” Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study

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