A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Tlig, Lotfi; Bouchouicha, Moez; Tlig, Mohamed; Sayadi, Mounir; Moreau, Éric

doi:10.3390/s20226429

Cited by 12 publications

(10 citation statements)

References 38 publications

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“…Classical segmentation methods mainly rely on features such as color, spatial structure and texture to label the pixels of the flame area. TLIG et al [13] proposed a corresponding image segmentation method based on principal component analysis and Gabor filter, which proposed a new superpixel extraction strategy, but it was insensitive to noise and needed to rely on manual feature design. The applicability in different scenarios was poor.…”

Section: Introductionmentioning

confidence: 99%

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Wang

Peng

2022

Forests

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In recent years, frequent forest fires have plagued countries all over the world, causing serious economic damage and human casualties. Faster and more accurate detection of forest fires and timely interventions have become a research priority. With the advancement in deep learning, fully convolutional network architectures have achieved excellent results in the field of image segmentation. More researchers adopt these models to segment flames for fire monitoring, but most of the works are aimed at fires in buildings and industrial scenarios. However, there are few studies on the application of various fully convolutional models to forest fire scenarios, and comparative experiments are inadequate. In view of the above problems, on the basis of constructing the dataset with remote-sensing images of forest fires captured by unmanned aerial vehicles (UAVs) and the targeted optimization of the data enhancement process, four classical semantic segmentation models and two backbone networks are selected for modeling and testing analysis. By comparing inference results and the evaluation indicators of models such as mPA and mIoU, we can find out the models that are more suitable for forest fire segmentation scenarios. The results show that the U-Net model with Resnet50 as a backbone network has the highest segmentation accuracy of forest fires with the best comprehensive performance, and is more suitable for scenarios with high-accuracy requirements; the DeepLabV3+ model with Resnet50 is slightly less accurate than U-Net, but it can still ensure a satisfying segmentation performance with a faster running speed, which is suitable for scenarios with high real-time requirements. In contrast, FCN and PSPNet have poorer segmentation performance and, hence, are not suitable for forest fire detection scenarios.

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Section: Introductionmentioning

confidence: 99%

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Wang

Peng

2022

Forests

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“…An input image is partitioned (or subdivided) into meaningful image objects (segments). Image segmentation can be classified into two categories: supervised (empirical discrepancy methods) and unsupervised (empirical goodness methods) [80]. Unsupervised approaches evaluate a segmentation result based on how well the image object matches a human perception of the desired set of segmented images, and they use quality criteria that are typically created in accordance with human perceptions of what constitutes a good segmentation.…”

Section: Image Segmentationmentioning

confidence: 99%

“…( Gabor filters are used to extract spatially localized spectral features [80]. They have been advocated for because they are based on principles found in similar human visual systems and have key features that can be utilized to segment images.…”

Section: ) Region Growing Segmentationmentioning

confidence: 99%

Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art

2022

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The advent of modern remote sensors alongside the development of advanced parallel computing has significantly transformed both the theoretical and real implementation aspects of remote sensing. Several algorithms for detecting objects of interest in remote sensing images and subsequent classification have been devised, and these include template matching based methods, machine learning and knowledge-based methods. Knowledge-driven approaches have received much attention from the remote sensing fraternity. They do, however, face challenges in terms of sensory gap, duality of expression, vagueness and ambiguity, geographic concepts expressed in multiple modes, and semantic gap. This paper aims to review and provide an up-to-date survey on machine learning and knowledge driven approaches towards remote sensing forest image analysis. It is envisaged that this work will assist researchers in coming up with efficient models that accurately detect and classify forest images. There is a mismatch between what domain experts expect from remote sensing data and what remote sensing science produces. Such a mismatch or disparity can be reduced or alleviated by adopting an ontology paradigm methodology. Ontologies should be used to support the future of remote sensing in forest object classification. The paper is presented in five parts: (1) a review of methods used for forest image detection and classification; (2) challenges faced by object detection methods; (3) analysis of segmentation techniques employed; (4) feature extraction and classification; and (5) performance of the state-of-the-art methods employed in forest image detection and classification.

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“…In recent years, the rise of satellite remote sensing technology has opened up more possibilities for continuous monitoring of forest cover. The current mainstream methods of monitoring forest cover by satellite remote sensing can be classified as the remote sensing index method [4], image transformation method [5], texture analysis method [6], object-based method [7], machine learning classification method, and so on [8]. The remote sensing index method is a commonly used method for monitoring forest changes.…”

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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A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Cited by 12 publications

References 38 publications

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art

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

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