Real-Time Big Data Analytical Architecture for Remote Sensing Application

Rathore, M. Mazhar; Paul, Anand; Ahmad, Awais; Chen, Bo-Wei; Huang, Bormin; Ji, Wen

doi:10.1109/jstars.2015.2424683

Cited by 159 publications

(68 citation statements)

References 28 publications

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“…We compute hidden layer z = S(D (1) x + b (1) ) with trainable parameters D (1) and b (1) , where S(·) is a nonlinear function. The output layer is then computed by a similar affine transformationx = D (2) z + b (2) . The objective is formulated by ensuringx to be a good reconstruction of input x:…”

Section: Auto-encodermentioning

confidence: 99%

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Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Xia

et al. 2016

Remote Sensing

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Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal "filters" from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

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Section: Auto-encodermentioning

confidence: 99%

“…In recent years, an increasing number of commercial satellite sensors of high resolution have been successfully launched, and a new era of "big data" for remote sensing is coming [1,2]. The more and more mature remote sensing imaging technologies have made massive raw high-resolution (HR) satellite and aerial image datasets available.…”

Section: Introductionmentioning

confidence: 99%

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Xia

et al. 2016

Remote Sensing

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“…The HSI can offer much richer information, and it can discriminate the subtle differences in different land cover types [5,6]. HSI plays a significant role in the application of anomaly detection, agricultural production, disaster warning, and land cover classification [7][8][9]. However, the traditional classification methods commonly cause the Hughes phenomena because of the high dimensional characteristics in HSI [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

et al. 2017

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Abstract:Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In this paper, we propose a new dimensionality reduction (DR) method, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. Firstly, LGSFA uses the intraclass neighbor points of each point to compute its reconstruction point. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass properties of hyperspectral data. Finally, the neighbor points and corresponding intraclass reconstruction points are used to enhance the intraclass-manifold compactness and the interclass-manifold separability.LGSFA can effectively reveal the intrinsic manifold structure and obtain the discriminating features of HSI data for classification. Experiments on the Salinas, Indian Pines, and Urban data sets show that the proposed LGSFA algorithm achieves the best classification results than other state-of-the-art methods.

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“…In recent years, different methods have been developed. The state-of-art methods could be divided into three typical categories: (1) outline or border extraction; (2) object detection/classification; and (3) image segmentation [9][10][11]. This paper focuses on the third category, i.e., the image scene is divided into road area and background area.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction from High Resolution Image with Deep Convolution Network—A Case Study of GF-2 Image

Wei

Zhang

Liu

et al. 2018

The 2nd International Electronic Conference on Remote Sensing

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Recently, with the development of remote sensing and computer techniques, automatic and accurate road extraction is becoming feasible for practical usage. Nowadays, accurate extraction of road information from satellite data has become one of the most popular topics in both remote sensing and transportation fields. It is very useful for applying this technique to fast map updating, construction supervision, and so on. However, as there is usually a huge volume of information provided by remote sensing data, an efficient method to refine the big volume of data is important in corresponding applications. We apply deep convolution network to perform an image segmentation approach, as a solution for extracting road networks from high resolution images. In order to take advantage of deep learning, we study the methods of generating representative training and testing datasets, and develop semi-supervised leaning skills to enhance the data scale. The extraction of the satellite images that are affected by color distortion is also studied, in order to make the method more robust for more applicational fields. The GF-2 satellite data is used for experiments, as its images may show optical distortion in small pieces. Experiments in this paper showed that, the proposed solution successfully identifies road networks from complex situations with a total accuracy of more than 80% in discriminable areas.

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Real-Time Big Data Analytical Architecture for Remote Sensing Application

Cited by 159 publications

References 28 publications

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

Road Extraction from High Resolution Image with Deep Convolution Network—A Case Study of GF-2 Image

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