7 research outputs found

    Multitemporal Relearning with Convolutional LSTM Models for Land Use Classification

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    In this article, we present a novel hybrid framework, which integrates spatial–temporal semantic segmentation with postclassification relearning, for multitemporal land use and land cover (LULC) classification based on very high resolution (VHR) satellite imagery. To efficiently obtain optimal multitemporal LULC classification maps, the hybrid framework utilizes a spatial–temporal semantic segmentation model to harness temporal dependency for extracting high-level spatial–temporal features. In addition, the principle of postclassification relearning is adopted to efficiently optimize model output. Thereby, the initial outcome of a semantic segmentation model is provided to a subsequent model via an extended input space to guide the learning of discriminative feature representations in an end-to-end fashion. Last, object-based voting is coupled with postclassification relearning for coping with the high intraclass and low interclass variances. The framework was tested with two different postclassification relearning strategies (i.e., pixel-based relearning and object-based relearning) and three convolutional neural network models, i.e., UNet, a simple Convolutional LSTM, and a UNet Convolutional-LSTM. The experiments were conducted on two datasets with LULC labels that contain rich semantic information and variant building morphologic features (e.g., informal settlements). Each dataset contains four time steps from WorldView-2 and Quickbird imagery. The experimental results unambiguously underline that the proposed framework is efficient in terms of classifying complex LULC maps with multitemporal VHR images

    Semi-supervised learning with constrained virtual support vector machines for classification of remote sensing image data

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    We introduce two semi-supervised models for the classification of remote sensing image data. The models are built upon the framework of Virtual Support Vector Machines (VSVM). Generally, VSVM follow a two-step learning procedure: A Support Vector Machines (SVM) model is learned to determine and extract labeled samples that constitute the decision boundary with the maximum margin between thematic classes, i.e., the Support Vectors (SVs). The SVs govern the creation of so-called virtual samples. This is done by modifying, i.e., perturbing, the image features to which a decision boundary needs to be invariant. Subsequently, the classification model is learned for a second time by using the newly created virtual samples in addition to the SVs to eventually find a new optimal decision boundary. Here, we extend this concept by (i) integrating a constrained set of semilabeled samples when establishing the final model. Thereby, the model constrainment, i.e., the selection mechanism for including solely informative semi-labeled samples, is built upon a self-learning procedure composed of two active learning heuristics. Additionally, (ii) we consecutively deploy semi-labeled samples for the creation of semi-labeled virtual samples by modifying the image features of semi-labeled samples that have become semi-labeled SVs after an initial model run. We present experimental results from classifying two multispectral data sets with a sub-meter geometric resolution. The proposed semi-supervised VSVM models exhibit the most favorable performance compared to related SVM and VSVM-based approaches, as well as (semi-)supervised CNNs, in situations with a very limited amount of available prior knowledge, i.e., labeled samples

    Object-based Morphological Profiles for Classification of Remote Sensing Imagery

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    Morphological operators (MOs) and their enhancements such as morphological profiles (MPs) are subject to a lively scientific contemplation since they are found to be beneficial for, for example, classification of very high spatial resolution panchromatic, multi-, and hyperspectral imagery. They account for spatial structures with differing magnitudes and, thus, provide a comprehensive multilevel description of an image. In this paper, we introduce the concept of object-based MPs (OMPs) to also encode shape-related, topological, and hierarchical properties of image objects in an exhaustive way. Thereby, we seek to benefit from the so-called object-based image analysis framework by partitioning the original image into objects with a segmentation algorithm on multiple scales. The obtained spatial entities (i.e., objects) are used to aggregate multiple sequences obtained with MOs according to statistical measures of central tendency. This strategy is followed to simultaneously preserve and characterize shape properties of objects and enable both the topological and hierarchical decompositions of an image with respect to the progressive application of MOs. Subsequently, supervised classification models are learned by considering this additionally encoded information. Experimental results are obtained with a random forest classifier with heuristically tuned hyperparameters and a wrapper-based feature selection scheme. We evaluated the results for two test sites of panchromatic WorldView-II imagery, which was acquired over an urban environment. In this setting, the proposed OMPs allow for significant improvements with respect to classification accuracy compared to standard MPs (i.e., obtained by paired sequences of erosion, dilation, opening, closing, opening by top-hat, and closing by top-hat operations)

    Proposta de classificação e de pós-classificação baseada em objetos da cobertura e do uso da terra por meio de imagens obtidas por Veículo Aéreo Não Tripulado (VANT)

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    Uma das principais aplicações das imagens de sensoriamento remoto é a classificação da cobertura e do uso da terra. Para mapeamentos mais detalhados, utilizam-se atualmente imagens aéreas obtidas por Veículo Aéreo Não Tripulado (VANT). No entanto, essas imagens apresentam uma alta variabilidade espectral intraclasse e entreclasses, dificultando a classificação da cobertura e do uso da terra. A partir dessas considerações, esta tese tem como objetivos: (i) desenvolver e avaliar um método de reconhecimento de padrões não paramétrico para classificação baseada em objetos da cobertura e do uso da terra, denominado de Iterative K – Nearest Neighbors Algorithm (IKNN); (ii) propor e avaliar dois métodos de pósclassificação que consideram o contexto dos objetos, intitulados como: Votação dos Objetos Vizinhos (VOV) e Quantificação das Fronteiras dos Objetos (QFO); e (iii) desenvolver uma ferramenta automatizada para classificação baseada em objetos que integre reconhecimento de padrões e Análise de Imagens Baseada em Objetos, chamada de GeoPatterns. Foi utilizada uma ortoimagem aérea obtida por um Veículo Aéreo Não Tripulado (VANT) Echar 20B. Essa ortoimagem foi segmentada utilizando o método crescimento de regiões. As ferramentas e os métodos propostos foram desenvolvidos utilizando a linguagem de programação Python e as bibliotecas: Scikit-Learn (mineração de dados), Numpy (computação científica) e PyQGIS (integra Python e QGIS). O método IKNN possibilitou a seleção das características mais relevantes e o tratamento da sobreposição dos seus valores. Quando utilizado um limiar de confiança igual a 60%, IKNN resultou em uma Proporção Correta (PC) igual a 90,0%, o que foi superior aos métodos Support Vector Machine (SVM) e k – Nearest Neighbors (k-NN). O método de pós-classificação baseada em objetos, VOV, aumentou a acurácia da classificação de 92,5% para 95,7%, quando avaliados objetos da segmentação maiores que 7000 pixels. O método de pós-classificação QFO obteve resultados superiores, alcançando acurácias iguais a 97,0% para objetos da classificação maiores que 9400 pixels. O programa GeoPatterns viabilizou a integração de técnicas não paramétricas de reconhecimento de padrões e OBIA, assim como automatizou os processos de segmentação, amostragem e classificação dos objetos. A interface gráfica tornou mais acessível a classificação baseada em objetos da cobertura e do uso da terra por meio de imagens com resolução espacial submétrica obtidas por VANT.One of the main applications of remote sensing images is both land cover and land use classifications. For more refined mapping, Unmanned Aerial Vehicle (UAV) aerial images are currently used. However, these images show both high intraclass and interclasses spectral variability, making it difficult to achieve land cover and land use classifications. Based on these considerations, this thesis aims: both to develop and to evaluate a nonparametric pattern recognition method for object-based land cover and land use classification, which is called Iterative K - Nearest Neighbors Algorithm (IKNN); both to propose and to evaluate two postclassification methods that consider the objects’ context, Voting Neighbors Objects (VNO) and Quantification of Object Frontiers (QOF); and to develop an automated tool for objectbased classification that integrates recognition patterns and Object-Based Image Analysis, called GeoPatterns. To do so, an aerial orthoimage was obtaining by an Unmanned Aerial Vehicle (UAV) Echar 20B. This orthoimage was segmenting by using region growing method. Both proposed tools and methods were developing by using Python programming language and Scikit-Learn (data mining), Numpy (scientific computing) and PyQGIS (which integrate Python and QGIS) libraries. The IKNN method allowed the selection of the most relevant characteristics and the treatment of the overlapping of its values. To the 60% confidence threshold, IKNN method resulted in a Correct Proportion equal to 90.0%, which was superior to the Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) methods. The VNO object-based post-classification method increased the accuracy of the classification from 92.5% to 95.7% in the evaluation of 7,000 pixels or higher segmentation objects. The QOF post-classification method obtained higher results, reaching up to 97.0% for 9,400 pixels or higher objects classification. The GeoPatterns program enabled the integration of both nonparametric recognition patterns and OBIA techniques, as well as it has automated the segmentation, the sampling and the classification of objects. By using UAV-obtained submetric resolution images, the graphical interface made both land cover and land use objectbased classification more accessible

    Object-Based Postclassification Relearning

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    —In this letter, we present an object-based postclassification relearning approach for enhanced supervised remote sensing image classification. Conventional postclassification processing techniques aim to enhance the classification accuracy by imposing smoothness priors in the image domain (based on, for example, majority filtering or Markov random fields). In contrast to that, here, a supervised classification model is learned for the second time, with additional information generated from the initial classification outcome to enhance the discriminative properties of relearned decision functions. This idea is followed within an object-based image analysis framework. Therefore, we model spatial-hierarchical context relations with the preliminary classification outcome by computing class-related features using a triplet of hierarchical segmentation levels. Those features are used to enlarge the initial feature space and impose spatial regularization in the relearned model. We evaluate the relevance of the method in the context of classifying of a high-resolution multispectral image, which was acquired over an urban environment. The experimental results show an enhanced classification accuracy using this method compared to both per-pixel-based approach and outcomes obtained with a conventional object-based postclassification processing technique (i.e., object-based voting)

    Multitemporal Relearning with Convolutional LSTM Models for Land Use Classification

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    In this article, we present a novel hybrid framework, which integrates spatial-Temporal semantic segmentation with postclassification relearning, for multitemporal land use and land cover (LULC) classification based on very high resolution (VHR) satellite imagery. To efficiently obtain optimal multitemporal LULC classification maps, the hybrid framework utilizes a spatial-Temporal semantic segmentation model to harness temporal dependency for extracting high-level spatial-Temporal features. In addition, the principle of postclassification relearning is adopted to efficiently optimize model output. Thereby, the initial outcome of a semantic segmentation model is provided to a subsequent model via an extended input space to guide the learning of discriminative feature representations in an end-To-end fashion. Last, object-based voting is coupled with postclassification relearning for coping with the high intraclass and low interclass variances. The framework was tested with two different postclassification relearning strategies (i.e., pixel-based relearning and object-based relearning) and three convolutional neural network models, i.e., UNet, a simple Convolutional LSTM, and a UNet Convolutional-LSTM. The experiments were conducted on two datasets with LULC labels that contain rich semantic information and variant building morphologic features (e.g., informal settlements). Each dataset contains four time steps from WorldView-2 and Quickbird imagery. The experimental results unambiguously underline that the proposed framework is efficient in terms of classifying complex LULC maps with multitemporal VHR images
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