Forest and Water Bodies Segmentation Through Satellite Images Using U-Net

Filatov, Dmytro; Yar, Ghulam Nabi Ahmad Hassan

doi:10.48550/arxiv.2207.11222

Cited by 1 publication

(3 citation statements)

References 12 publications

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“…In light of the evaluation of the algorithms, the Random Forest algorithm outperformed Linear Discriminant Analysis, Gaussian Naive Bayes, and Support vector machines algorithms in terms of accuracy, Jaccard score, and ROC curves. As presented in Table 12 the proposed model in this study outperformed other models from related studies [10,[42][43][44][45][46]. However, the Unet semantic segmentation in [47] segmenting the forest images and predicting any loss (deforestation) or gain (reforestation) slightly outperformed our model with 95% accuracy.…”

Section: Evaluation Of Machine Learning Models In Detecting Non-fores...mentioning

confidence: 62%

“…Unet with spatial pyramid spooling [42] 86.71 75.59 Hnet with Inception as backbone [43] 68 83 Deep Convolutional Neural Networks (DCNN) [48] 91 -Unet for forest segmentation [10] 91 -SENet and MobileNet embedded in DeepLabV3+ (SMED) [44] 82.95 60 improved tuna swarm optimization (ITSO) [46] -59 Unet semantic segmentation [47] 95 -Random Forest 94 91…”

Section: Methods Accuracy Ioumentioning

confidence: 99%

“…Segmenting forest, land cover and satellite images have been of interest to many researchers [ 10 , 11 , 12 , 13 ] in recent years. A study [ 10 ] proposed a U-net model to perform segmentation tasks on forest and water bodies satellite images. The purpose of the model was to determine the area covered by forest and water.…”

Section: Related Studiesmentioning

confidence: 99%

See 2 more Smart Citations

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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