An Automatized Frequency Analysis for Vine Plot Detection and Delineation in Remote Sensing

Delenne, Carole; Rabatel, Gilles; Deshayes, M.

doi:10.1109/lgrs.2008.916065

Cited by 15 publications

(12 citation statements)

References 10 publications

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“…(1) three statistical local texture descriptors: the gray-level co-occurrence matrix (GLCM) [1,5,6], the Gabor filter banks (GFB) [2,7,8] and the local Weber descriptor (WLD) [3,11]. The GLCM and GFB appear to be two of the most widely-used methods for texture analysis in remote sensing imagery.…”

Section: Retrieval Resultsmentioning

confidence: 99%

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Texture Retrieval from VHR Optical Remote Sensed Images Using the Local Extrema Descriptor with Application to Vineyard Parcel Detection

Pham

Mercier

Regniers³

et al. 2016

Remote Sensing

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Abstract:In this article, we develop a novel method for the detection of vineyard parcels in agricultural landscapes based on very high resolution (VHR) optical remote sensing images. Our objective is to perform texture-based image retrieval and supervised classification algorithms. To do that, the local textural and structural features inside each image are taken into account to measure its similarity to other images. In fact, VHR images usually involve a variety of local textures and structures that may verify a weak stationarity hypothesis. Hence, an approach only based on characteristic points, not on all pixels of the image, is supposed to be relevant. This work proposes to construct the local extrema-based descriptor (LED) by using the local maximum and local minimum pixels extracted from the image. The LED descriptor is formed based on the radiometric, geometric and gradient features from these local extrema. We first exploit the proposed LED descriptor for the retrieval task to evaluate its performance on texture discrimination. Then, it is embedded into a supervised classification framework to detect vine parcels using VHR satellite images. Experiments performed on VHR panchromatic PLEIADES image data prove the effectiveness of the proposed strategy. Compared to state-of-the-art methods, an enhancement of about 7% in retrieval rate is achieved. For the detection task, about 90% of vineyards are correctly detected.

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Section: Retrieval Resultsmentioning

confidence: 99%

“…The GLCM and GFB appear to be two of the most widely-used methods for texture analysis in remote sensing imagery. They have been adopted for the vine detection task within the last ten years [5][6][7][8].…”

Section: Retrieval Resultsmentioning

confidence: 99%

Texture Retrieval from VHR Optical Remote Sensed Images Using the Local Extrema Descriptor with Application to Vineyard Parcel Detection

Pham

Mercier

Regniers³

et al. 2016

Remote Sensing

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“…This makes DSM/NDVI appealing as a black box concept. This is not the case for certain unsupervised methodologies [17,18] or indeed specialized convolutional neural networks with high training cost (see [22]). The specially adapted nature of such methods tends to overfit their generic problem types and as a consequence renders them difficult to adjust to different problem scenarios.…”

Section: The Dsm/radiometric Approach To Canopy Segmentationmentioning

confidence: 99%

“…Especially the response of the soil in the presence of grass produces incorrect results considering only the spectral response of the bare soil compared to the grassland. In non-radiometric approaches crop/soil detection is usually carried out by using parametric algorithms based on feature classification by hand, for example frequency analysis [17], Hough space clustering or total least squares as in [18]. These methodologies have their limitations so that for the production of high quality prescription maps today fully automated techniques are used.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

et al. 2019

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One of the most challenging problems in precision agriculture is to correctly identify and separate crops from the soil. Current precision farming algorithms based on artificially intelligent networks use multi-spectral or hyper-spectral data to derive radiometric indices that guide the operational management of agricultural complexes. Deep learning applications using these big data require sensitive filtering of raw data to effectively drive their hidden layer neural network architectures. Threshold techniques based on the normalized difference vegetation index (NDVI) or other similar metrics are generally used to simplify the development and training of deep learning neural networks. They have the advantage of being natural transformations of hyper-spectral or multi-spectral images that filter the data stream into a neural network, while reducing training requirements and increasing system classification performance. In this paper, to calculate a detailed crop/soil segmentation based on high resolution Digital Surface Model (DSM) data, we propose the redefinition of the radiometric index using a directional mathematical filter. To further refine the analysis, we feed this new radiometric index image of about 3500 × 4500 pixels into a relatively small Convolution Neural Network (CNN) designed for general image pattern recognition at 28 × 28 pixels to evaluate and resolve the vegetation correctly. We show that the result of applying a DSM filter to the NDVI radiometric index before feeding it into a Convolutional Neural Network can potentially improve crop separation hit rate by 65%.

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“…Many studies have addressed this challenge by considering the planting patterns in the landscape as textures and by computing statistical features on these textures to improve classification or segmentation results on VHR remotely sensed images. In these studies, common tools to characterize textures are Grey Level Cooccurrence Matrices (GLCM) [1][2], variograms [3], Gabor filters [4][5], wavelet representations [6][7], etc. These approaches all lead to a small-sized representation of the textural content of an image since a few descriptors ˗˗˗ generally referred to as textural signature ˗˗˗ are extracted.…”

Section: Introductionmentioning

confidence: 99%

Texture based image retrieval and classification of very high resolution maritime pine forest images

Regniers

Costa

Grenier

et al. 2013

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS

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Textural analysis can bring valuable information in the classification or the segmentation process of land covers displaying regular patterns in very high resolution remotely sensed images. In this study, we investigate how features extracted by multivariate modeling of the local spatial dependence in the wavelet domain can efficiently capture the textural content of maritime pine forest images in comparison with a commonly used texture analysis approach, the GLCM. To evaluate the performances of the tested methods, we used a content based image retrieval framework and created a database of image patches representing different development stages of the forest stands. Results show that multivariate models display higher retrieval rates than GLCM-based methods with yet a higher sensitivity to the dominant orientation in anisotropic textures. These observations open up new perspectives in the use of multivariate modeling for textural features extraction in very high resolution image classification.Index Terms-textural feature, multivariate models, GLCM, very high resolution, forest.

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An Automatized Frequency Analysis for Vine Plot Detection and Delineation in Remote Sensing

Cited by 15 publications

References 10 publications

Texture Retrieval from VHR Optical Remote Sensed Images Using the Local Extrema Descriptor with Application to Vineyard Parcel Detection

Texture Retrieval from VHR Optical Remote Sensed Images Using the Local Extrema Descriptor with Application to Vineyard Parcel Detection

Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

Texture based image retrieval and classification of very high resolution maritime pine forest images

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