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

Regniers, Olivier; Costa, Jean-Pierre da; Grenier, Gilbert; Germain, Christian; Bombrun, Lionel

doi:10.1109/igarss.2013.6723719

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…Meanwhile, the WLD is one of the most recent local descriptors in computer vision; (2) three distribution models of wavelet coefficients: the multivariate Gaussian model (MGM), the spherically-invariant random vectors (SIRV) and the Gaussian copula-based model (GCM) [12,13]. These methods are the most recent wavelet-based techniques proposed for tackling texture-based retrieval and vine detection tasks.…”

Section: Retrieval Resultsmentioning

confidence: 99%

“…We note that the implementations of the three model-based techniques (i.e., MGM, SIRV, GCM) are inherited from [12,13]. Then, the three statistical descriptors (GLCM, GFB, WLD) are implemented using a keypoint-based approach.…”

Section: Retrieval Resultsmentioning

confidence: 99%

“…Figures 5 and 6 provide the supervised classification and vine detection results yielded by the proposed LED descriptor compared to several reference methods. Here, we show the results produced by two wavelet-based techniques: the multivariate Gaussian model (MGM) and the Gaussian copula model [12,13]; and three classical statistical methods: the gray-level co-occurrence matrix (GLCM) [1], the Gabor filter banks [2] and the Weber local descriptors (WLD) [3]. The implementations of these reference methods were carried out similarly to the previous retrieval task.…”

Section: 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%

Section: Retrieval Resultsmentioning

confidence: 99%

Section: 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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show abstract

“…In the year 2003, researchers reviewed the total methods of the shape representation and descriptions [6]. Multivariate models and Wavelet features were utilized for various applications such as finger print analysis [7][8][9][10]. Researchers have proposed Color Guided Vehicle (CGV) which uses generalized pixel method for comparing color of the object or work piece and the color of the destination or work station [11].…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval System Using Intuitive Descriptors

Joshi¹,

Bhavsar²,

Sanghvi³

2014

Procedia Technology

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With the increase in the popularity of using huge image databases in various image retrieval applications, a need arises to develop an efficient, robust and automatic system which provides output in the form of similar images with respect to the input or query image. In this paper, three Image Retrieval Systems using very basic but novel descriptors, which can retrieve black and white (binary) image, grayscale image and color image from the binary, grayscale and color image databases, respectively, were implemented. It should be noted that the features used for the binary image and grayscale image may not be efficiently applicable to the color images. Obtained results indicate the same and it additionally shows that irrespective of numbers of images in database, developed Image Retrieval Systems retrieve images in very short period of time with high accuracy. Accuracy in color image retrieval is less compared to binary and grayscale image retrieval. So, it creates a need to design highly effective features for the color image retrieval.

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“…In conventional texture features used for CBIR, there is a gray level co-occurrence matrix [25], [20], [11], Wavelet transforms [21], Local Binary Pattern [5], Tamura's texture [9], Gabor's Filters [9], Markov Random Field [36], [34].…”

Section: Figure 1: Categories Of the Low Level Featuresmentioning

confidence: 99%

Development of Nanosatellite based Image Retrieval system

Gashayija¹,

Bierman²

2014

IJCA

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Rapid development of nanosatellites has led to many unique and innovative space applications. These tiny satellites have come a long way since Sputnik, the first satellite that was launched in 1957, weighing 83 kg. The success of the CubeSat has revolutionized space technology. Typically, these CubeSat fall into categories of nanosatellites weighing no more than 1.33 Kg and using less power than a five watt light bulb. Mostly, CubeSat has one or more payloads such as Imaging Payload, Scientific payload and High Frequency (HF) Radio Beacon.With the Imaging payload their task is to capture low or high resolution images of the earth observation missions. With the steadily increasing demand for CubeSat imaging payload missions, several nano-satellites have been launched, and thousands of low/high resolution images are acquired every day and transmitted to ground stations. This leads to an exponential increase in the number of low/high resolution images in databases. Therefore, how to retrieve useful images quickly and accurately from a huge and unstructured image database becomes a challenge.In this project, it proposes the use of a content-based image retrieval (CBIR) system relying on a combination of the three low level features such as color, shape and texture features. In order to accurately classify and retrieve useful information in a huge image database. The evaluation performance of the proposed system when compared to other existing systems provides a precision value of 1 or 100%. It can be concluded that the proposed system is able to classify images and retrieve results in a reasonable time of at least 15 seconds.

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

Cited by 13 publications

References 12 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

Image Retrieval System Using Intuitive Descriptors

Development of Nanosatellite based Image Retrieval system

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