Tree Species Classification by Fusing of Very Highresoltuion Hyperspectral Images and 3K-DSM

Yuan, Xiangtian; Tian, Jiaojiao; Cerra, Daniele; Meynberg, Oliver; Kempf, Christian; Reinartz, Peter

doi:10.1109/whispers.2018.8747246

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…Other works use simple texture methods for vegetation classification, specifically LBP and GLCM. For example, the authors of [12] applied the LBP textures for the classification of tree species using hyperspectral data and an aerial stereo camera system. In the classification step, a pixel-based approach and a patch-based BoW approach were used.…”

Section: Discussionmentioning

confidence: 99%

“…Two simple methods for texture extraction, based on the analysis of patterns in the neighborhood of a pixel, are Local Binary Pattern (LBP) and Gray-Level Co-occurrence Matrix (GLCM). LBP is used in [12] for the classification of tree species using hyperspectral data and an aerial stereo camera system. Feature extraction is performed following a patch-based approach.…”

Section: Introductionmentioning

confidence: 99%

“…For the classification of a image using textures it is necessary to delimit regions over which the texture features are computed. Most of the vegetation classification methods proposed in the literature use regular patches [6,8,10,12,13,15,16,19]. In other cases, segmentation or object detection algorithms for dividing the image into regions are used [14,17,18].…”

Section: Introductionmentioning

confidence: 99%

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Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

2020

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Texture information allows characterizing the regions of interest in a scene. It refers to the spatial organization of the fundamental microstructures in natural images. Texture extraction has been a challenging problem in the field of image processing for decades. In this paper, different techniques based on the classic Bag of Words (BoW) approach for solving the texture extraction problem in the case of hyperspectral images of the Earth surface are proposed. In all cases the texture extraction is performed inside regions of the scene called superpixels and the algorithms profit from the information available in all the bands of the image. The main contribution is the use of superpixel segmentation to obtain irregular patches from the images prior to texture extraction. Texture descriptors are extracted from each superpixel. Three schemes for texture extraction are proposed: codebook-based, descriptor-based, and spectral-enhanced descriptor-based. The first one is based on a codebook generator algorithm, while the other two include additional stages of keypoint detection and description. The evaluation is performed by analyzing the results of a supervised classification using Support Vector Machines (SVM), Random Forest (RF), and Extreme Learning Machines (ELM) after the texture extraction. The results show that the extraction of textures inside superpixels increases the accuracy of the obtained classification map. The proposed techniques are analyzed over different multi and hyperspectral datasets focusing on vegetation species identification. The best classification results for each image in terms of Overall Accuracy (OA) range from 81.07% to 93.77% for images taken at a river area in Galicia (Spain), and from 79.63% to 95.79% for a vast rural region in China with reasonable computation times.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

2020

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“…[18,19]. Two simple methods for texture extraction, based on the analysis of patterns in the neighborhood of a pixel, are local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM) [20,21].…”

Section: Introductionmentioning

confidence: 99%

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

et al. 2021

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Watershed management is the study of the relevant characteristics of a watershed aimed at the use and sustainable management of forests, land, and water. Watersheds can be threatened by deforestation, uncontrolled logging, changes in farming systems, overgrazing, road and track construction, pollution, and invasion of exotic plants. This article describes a procedure to automatically monitor the river basins of Galicia, Spain, using five-band multispectral images taken by an unmanned aerial vehicle and several image processing algorithms. The objective is to determine the state of the vegetation, especially the identification of areas occupied by invasive species, as well as the detection of man-made structures that occupy the river basin using multispectral images. Since the territory to be studied occupies extensive areas and the resulting images are large, techniques and algorithms have been selected for fast execution and efficient use of computational resources. These techniques include superpixel segmentation and the use of advanced texture methods. For each one of the stages of the method (segmentation, texture codebook generation, feature extraction, and classification), different algorithms have been evaluated in terms of speed and accuracy for the identification of vegetation and natural and artificial structures in the Galician riversides. The experimental results show that the proposed approach can achieve this goal with speed and precision.

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Tree Species Classification by Fusing of Very Highresoltuion Hyperspectral Images and 3K-DSM

Cited by 2 publications

References 16 publications

Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

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