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Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 22 [10/10/2022] . - pp 6643 - 6670 Titre : Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information Type de document : Article/Communication Auteurs : Ozlem Akar, Auteur ; Esra Tunc Gormus, Auteur Année de publication : 2022 Article en page(s) : pp 6643 - 6670 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] carte de la végétation [Termes IGN] classification orientée objet [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] filtre de Gabor [Termes IGN] image hyperspectrale [Termes IGN] matrice de co-occurrence [Termes IGN] niveau de gris (image) [Termes IGN] texture d'image [Termes IGN] transformation en ondelettes [Termes IGN] Turquie Résumé : (auteur) Land use and Land cover (LULC) mapping is one of the most important application areas of remote sensing which requires both spectral and spatial resolutions in order to decrease the spectral ambiguity of different land cover types. Airborne hyperspectral images are among those data which perfectly suits to that kind of applications because of their high number of spectral bands and the ability to see small details on the field. As this technology has newly developed, most of the image processing methods are for the medium resolution sensors and they are not capable of dealing with high resolution images. Therefore, in this study a new framework is proposed to improve the classification accuracy of land use/cover mapping applications and to achieve a greater reliability in the process of mapping land use map using high resolution hyperspectral image data. In order to achieve it, spatial information is incorporated together with spectral information by exploiting feature extraction methods like Grey Level Co-occurrence Matrix (GLCM), Gabor and Morphological Attribute Profile (MAP) on dimensionally reduced image with highest accuracy. Then, machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM) are used to investigate the contribution of texture information in the classification of high resolution hyperspectral images. In addition to that, further analysis is conducted with object based RF classification to investigate the contribution of contextual information. Finally, overall accuracy, producer’s/user’s accuracy, the quantity and allocation based disagreements and location and quantity based kappa agreements are calculated together with McNemar tests for the accuracy assessment. According to our results, proposed framework which incorporates Gabor texture information and exploits Discrete Wavelet Transform based dimensionality reduction method increase the overall classification accuracy up to 9%. Amongst individual classes, Gabor features boosted classification accuracies of all the classes (soil, road, vegetation, building and shadow) to 7%, 6%, 6%, 8%, 9%, and 24% respectively with producer’s accuracy. Besides, 17% and 10% increase obtained in user’s accuracy with MAP (area) feature in classifying road and shadow classes respectively. Moreover, when the object based classification is conducted, it is seen that the OA of pixel based classification is increased further by 1.07%. An increase between 2% and 4% is achieved with producer’s accuracy in soil, vegetation and building classes and an increase between 1% and 3% is achieved by user’s accuracy in soil, road, vegetation and shadow classes. In the end, accurate LULC map is produced with object based RF classification of gabor features added airborne hyperspectral image which is dimensionally reduced with DWT method. Numéro de notice : A2022-729 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1944453 Date de publication en ligne : 09/11/2021 En ligne : https://doi.org/10.1080/10106049.2021.1944453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101675 [article]

Adversarial defenses for object detectors based on Gabor convolutional layers / Abdollah Amirkhani in The Visual Computer, vol 38 n° 6 (June 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 6 (June 2022) . - pp 1929 - 1944 Titre : Adversarial defenses for object detectors based on Gabor convolutional layers Type de document : Article/Communication Auteurs : Abdollah Amirkhani, Auteur ; Mohammad Karimi, Auteur Année de publication : 2022 Article en page(s) : pp 1929 - 1944 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] détection d'objet [Termes IGN] filtre de Gabor [Termes IGN] reconnaissance de formes [Termes IGN] réseau antagoniste génératif Résumé : (auteur) Despite their many advantages and positive features, the deep neural networks are extremely vulnerable against adversarial attacks. This drawback has substantially reduced the adversarial accuracy of the visual object detectors. To make these object detectors robust to adversarial attacks, a new Gabor filter-based method has been proposed in this paper. This method has then been applied on the YOLOv3 with different backbones, the SSD with different input sizes and on the FRCNN; and thus, six robust object detector models have been presented. In order to evaluate the efficacy of the models, they have been subjected to adversarial training via three types of targeted attacks (TOG-fabrication, TOG-vanishing, and TOG-mislabeling) and three types of untargeted random attacks (DAG, RAP, and UEA). The best average accuracy (49.6%) was achieved by the YOLOv3-d model, and for the PASCAL VOC dataset. This is far superior to the best performance and accuracy and obtained in previous works (25.4%). Empirical results show that, while the presented approach improves the adversarial accuracy of the object detector models, it does not affect the performance of these models on clean data. Numéro de notice : A2022-382 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02256-6 Date de publication en ligne : 24/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02256-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100651 [article]

Spectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation / Seyyed Ali Ahmadi in Geocarto international, vol 37 n° 7 ([15/04/2022])  

Public [article]  inGeocarto international > vol 37 n° 7 [15/04/2022] . - pp 2031 - 2054 Titre : Spectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation Type de document : Article/Communication Auteurs : Seyyed Ali Ahmadi, Auteur ; Nasser Mehrshad, Auteur ; Seyyed Mohammadali Arghavan, Auteur Année de publication : 2022 Article en page(s) : pp 2031 - 2054 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de sensibilité [Termes IGN] apprentissage profond [Termes IGN] données étiquetées d'entrainement [Termes IGN] échantillonnage de données [Termes IGN] filtre de Gabor [Termes IGN] image hyperspectrale Résumé : (auteur) Recently, deep learning (DL)-based methods have attracted increasing attention for hyperspectral images (HSIs) classification. However, the complex structure and limited number of labelled training samples of HSIs negatively affect the performance of DL models. In this paper, a spectral-spatial classification method is proposed based on the combination of local and global spatial information, including extended multi-attribute profiles and multiscale Gabor features, with sparse stacked autoencoder (GEAE). GEAE stacks the spatial and spectral information to form the fused features. Also, GEAE generates virtual samples using weighted average of available samples for expanding the training set so that many parameters of DL network can be learned optimally in limited labelled samples situations. Therefore, the similarity between samples is determined with distance metric learning to overcome the problems of Euclidean distance-based similarity metrics. The experimental results on three HSIs datasets demonstrate the effectiveness of the GEAE in comparison to some existing classification methods. Numéro de notice : A2022-498 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1797188 Date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1080/10106049.2020.1797188 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100990 [article]

Flexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10394 - 10409 Titre : Flexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Qingqing Zhao, Auteur ; Jiayue Zhuang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 10394 - 10409 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] classification non dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtre de Gabor [Termes IGN] image hyperspectrale [Termes IGN] ondelette de Gabor [Termes IGN] segmentation d'image [Termes IGN] superpixel Résumé : (auteur) Hyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG- Su ULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial–spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called Su ULDA, is skillfully introduced to reduce the extracted large amount of FG features. The Su ULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG- Su ULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG- Su ULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility. Numéro de notice : A2021-872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3048994 Date de publication en ligne : 20/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3048994 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99131 [article]

Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features / Bai Zhu in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 129 - 147 Titre : Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features Type de document : Article/Communication Auteurs : Bai Zhu, Auteur ; Yuanxin Ye, Auteur ; Liang Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 129 - 147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme ICP [Termes IGN] correction géométrique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] élément d'orientation externe [Termes IGN] enregistrement de données [Termes IGN] filtre de Gabor [Termes IGN] image aérienne [Termes IGN] recalage d'image [Termes IGN] semis de points [Termes IGN] SIFT (algorithme) [Termes IGN] structure-from-motion Résumé : (auteur) Co-registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quite challenging because the different imaging mechanisms produce significant geometric and radiometric distortions between the two multimodal data sources. To address this problem, we propose a robust and effective coarse-to-fine registration method that is conducted in two stages utilizing spatial constraints and Gabor structural features. In the first stage, the LiDAR point cloud data is transformed into an intensity map that is used as the reference image. Then, coarse registration is completed by designing a partition-based Features from Accelerated Segment Test (FAST) operator to extract the uniformly distributed interest points in the aerial images and thereafter performing a local geometric correction based on the collinearity equations using the exterior orientation parameters (EoPs). The coarse registration aims to provide a reliable spatial geometry relationship for the subsequent fine registration and is designed to eliminate rotation and scale changes, as well as making only a few translation differences exist between the images. In the second stage, a novel feature descriptor called multi-Scale and multi-Directional Features of odd Gabor (SDFG) is first built to capture the multi-scale and multi-directional structural properties of the images. Then, the three-dimensional (3D) phase correlation (PC) of the SDFG descriptor is established to detect the control points (CPs) between the aerial and LiDAR intensity image in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT) technique. Finally, the obtained CPs not only are employed to refine the EoPs, but also are used to achieve the fine registration of the aerial images and LiDAR data. We conduct experiments to verify the robustness of the proposed registration method using three sets of aerial images and LiDAR data with different scene coverage. Experimental results show that the proposed method is robust to geometric distortions and radiometric changes. Moreover, it achieves the registration accuracy of less than 2 pixels for all cases, which outperforms the current four state-of-the-art methods, demonstrating its superior registration performance. Numéro de notice : A2021-773 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.09.010 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98830 [article]

A feature based change detection approach using multi-scale orientation for multi-temporal SAR images / R. Vijaya Geetha in European journal of remote sensing, vol 54 sup 2 (2021)  

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Unsupervised vision methods based on image perceptual information / Eric Bazan (2021)  

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Superpixel-based multitask learning framework for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

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Deep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

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Discriminative low-rank Gabor filtering for spectral–spatial hyperspectral image classification / Lin He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

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Unsupervised segmentation of high-resolution remote sensing images based on classical models of the visual receptive field / Miaozhong Xu in Geocarto international, vol 30 n° 9 - 10 (October - November 2015)  

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Local binary patterns and extreme learning machine for hyperspectral imagery classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)  

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Gabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

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Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation / L. Wang in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)  

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Information content of very high resolution SAR images: study of feature extraction and imaging parameters / Corneliu Dimitru in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)  

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