4,931 research outputs found
Remote Sensing Image Representation based on Hierarchical Histogram Propagation
International audienceMany methods have been recently proposed to deal with the large amount of data provided by high- resolution remote sensing technologies. Several of these methods rely on the use of image segmentation algorithms for delineating target objects. However, a common issue in geographic object-based applications is the definition of the appropriate data representation scale, a problem that can be addressed by exploiting multiscale segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting histogram-based features from a hierarchy of regions for multiscale classification. The strategy, called H-Propagation, exploits the existing relation- ships among regions in a hierarchy to iteratively prop- agate features along multiple scales. The proposed method speeds up the feature extraction process and yields good results when compared with global low- level extraction approaches
Context guided belief propagation for remote sensing image classification.
We propose a context guided belief propagation (BP) algorithm to perform high spatial resolution multispectral imagery (HSRMI) classification efficiently utilizing superpixel representation. One important characteristic of HSRMI is that different land cover objects possess a similar spectral property. This property is exploited to speed up the standard BP (SBP) in the classification process. Specifically, we leverage this property of HSRMI as context information to guide messages passing in SBP. Furthermore, the spectral and structural features extracted at the superpixel level are fed into a Markov random field framework to address the challenge of low interclass variation in HSRMI classification by minimizing the discrete energy through context guided BP (CBP). Experiments show that the proposed CBP is significantly faster than the SBP while retaining similar performance as compared with SBP. Compared to the baseline methods, higher classification accuracy is achieved by the proposed CBP when the context information is used with both spectral and structural features
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These peculiarities lead to particular signal processing problems,
mainly characterized by indetermination and complex manifolds. The framework of
statistical learning has gained popularity in the last decade. New methods have
been presented to account for the spatial homogeneity of images, to include
user's interaction via active learning, to take advantage of the manifold
structure with semisupervised learning, to extract and encode invariances, or
to adapt classifiers and image representations to unseen yet similar scenes.
This tutuorial reviews the main advances for hyperspectral remote sensing image
classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution
In many computer vision applications, obtaining images of high resolution in
both the spatial and spectral domains are equally important. However, due to
hardware limitations, one can only expect to acquire images of high resolution
in either the spatial or spectral domains. This paper focuses on hyperspectral
image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low
spatial resolution (LR) but high spectral resolution is fused with a
multispectral image (MSI) with high spatial resolution (HR) but low spectral
resolution to obtain HR HSI. Existing deep learning-based solutions are all
supervised that would need a large training set and the availability of HR HSI,
which is unrealistic. Here, we make the first attempt to solving the HSI-SR
problem using an unsupervised encoder-decoder architecture that carries the
following uniquenesses. First, it is composed of two encoder-decoder networks,
coupled through a shared decoder, in order to preserve the rich spectral
information from the HSI network. Second, the network encourages the
representations from both modalities to follow a sparse Dirichlet distribution
which naturally incorporates the two physical constraints of HSI and MSI.
Third, the angular difference between representations are minimized in order to
reduce the spectral distortion. We refer to the proposed architecture as
unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results
demonstrate the superior performance of uSDN as compared to the
state-of-the-art.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2018, Spotlight
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