64 research outputs found
Impact of Feature Representation on Remote Sensing Image Retrieval
Remote sensing images are acquired using special platforms, sensors and are classified as aerial, multispectral and hyperspectral images. Multispectral and hyperspectral images are represented using large spectral vectors as compared to normal Red, Green, Blue (RGB) images. Hence, remote sensing image retrieval process from large archives is a challenging task. Remote sensing image retrieval mainly consist of feature representation as first step and finding out similar images to a query image as second step. Feature representation plays important part in the performance of remote sensing image retrieval process. Research work focuses on impact of feature representation of remote sensing images on the performance of remote sensing image retrieval. This study shows that more discriminative features of remote sensing images are needed to improve performance of remote sensing image retrieval process
Filter banks for hyperspectral pixel classification of satellite images
Satellite hyperspectral imaging deals with heterogenous images containing different texture areas. Filter banks are frequently used to characterize textures in the image performing pixel classification. This filters are designed using
Different scales and orientations in order to cover all areas in the frequential domain. This work is aimed at studying the influence of the different scales used in the analysis, comparing texture analysis theory with hyperspectral imaging necessities. To pursue this, Gabor filters over complex planes and opponent features are taken into account and also compared in the feature extraction proces
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
Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images
International audienceVarious approaches have been proposed in the literature for texture characterization of images. Some of them are based on statistical properties, others on fractal measures and some more on multi-resolution analysis. Basically, these approaches have been applied on mono-band images. However, most of them have been extended by including the additional information between spectral bands to deal with multi-band texture images. In this article, we investigate the problem of texture characterization for multi-band images. Therefore, we aim to add spectral information to classical texture analysis methods that only treat gray-level spatial variations. To achieve this goal, we propose a spatial and spectral gray level dependence method (SSGLDM) in order to extend the concept of gray level co-occurrence matrix (GLCM) by assuming the presence of texture joint information between spectral bands. Thus, we propose new multi-dimensional functions for estimating the second-order joint conditional probability density of spectral vectors. Theses functions can be represented in structure form which can help us to compute the occurrences while keeping the corresponding components of spectral vectors. In addition, new texture features measurements related to (SSGLDM) which define the multi-spectral image properties are proposed. Extensive experiments have been carried out on 624 textured multi-spectral images for use in prostate cancer diagnosis and quantitative results showed the efficiency of this method compared to the GLCM. The results indicate a significant improvement in terms of global accuracy rate. Thus, the proposed approach can provide clinically useful information for discriminating pathological tissue from healthy tissue
Spectral-spatial self-attention networks for hyperspectral image classification.
This study presents a spectral-spatial self-attention network (SSSAN) for classification of hyperspectral images (HSIs), which can adaptively integrate local features with long-range dependencies related to the pixel to be classified. Specifically, it has two subnetworks. The spatial subnetwork introduces the proposed spatial self-attention module to exploit rich patch-based contextual information related to the center pixel. The spectral subnetwork introduces the proposed spectral self-attention module to exploit the long-range spectral correlation over local spectral features. The extracted spectral and spatial features are then adaptively fused for HSI classification. Experiments conducted on four HSI datasets demonstrate that the proposed network outperforms several state-of-the-art methods
Large kernel spectral and spatial attention networks for hyperspectral image classification.
Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI
Spectral-spatial Feature Extraction for Hyperspectral Image Classification
As an emerging technology, hyperspectral imaging provides huge
opportunities in both remote sensing and computer vision. The
advantage of hyperspectral imaging comes from the high resolution
and wide range in the electromagnetic spectral domain which
reflects the intrinsic properties of object materials. By
combining spatial and spectral information, it is possible to
extract more comprehensive and discriminative representation for
objects of interest than traditional methods, thus facilitating
the basic pattern recognition tasks, such as object detection,
recognition, and classification. With advanced imaging
technologies gradually available for universities and industry,
there is an increased demand to develop new methods which can
fully explore the information embedded in hyperspectral images.
In this thesis, three spectral-spatial feature extraction methods
are developed for salient object detection, hyperspectral face
recognition, and remote sensing image classification.
Object detection is an important task for many applications based
on hyperspectral imaging. While most traditional methods rely on
the pixel-wise spectral response, many recent efforts have been
put on extracting spectral-spatial features. In the first
approach, we extend Itti's visual saliency model to the spectral
domain and introduce the spectral-spatial distribution based
saliency model for object detection. This procedure enables the
extraction of salient spectral features in the scale space, which
is related to the material property and spatial layout of
objects.
Traditional 2D face recognition has been studied for many years
and achieved great success. Nonetheless, there is high demand to
explore unrevealed information other than structures and textures
in spatial domain in faces. Hyperspectral imaging meets such
requirements by providing additional spectral information on
objects, in completion to the traditional spatial features
extracted in 2D images. In the second approach, we propose a
novel 3D high-order texture pattern descriptor for hyperspectral
face recognition, which effectively exploits both spatial and
spectral features in hyperspectral images. Based on the local
derivative pattern, our method encodes hyperspectral faces with
multi-directional derivatives and binarization function in
spectral-spatial space. Compared to traditional face recognition
methods, our method can describe distinctive micro-patterns which
integrate the spatial and spectral information of faces.
Mathematical morphology operations are limited to extracting
spatial feature in two-dimensional data and cannot cope with
hyperspectral images due to so-called ordering problem. In the
third approach, we propose a novel multi-dimensional morphology
descriptor, tensor morphology profile~(TMP), for hyperspectral
image classification. TMP is a general framework to extract
multi-dimensional structures in high-dimensional data. The
n-order morphology profile is proposed to work with the n-order
tensor, which can capture the inner high order structures. By
treating a hyperspectral image as a tensor, it is possible to
extend the morphology to high dimensional data so that powerful
morphological tools can be used to analyze hyperspectral images
with fused spectral-spatial information.
At last, we discuss the sampling strategy for the evaluation of
spectral-spatial methods in remote sensing hyperspectral image
classification. We find that traditional pixel-based random
sampling strategy for spectral processing will lead to unfair or
biased performance evaluation in the spectral-spatial processing
context. When training and testing samples are randomly drawn
from the same image, the dependence caused by overlap between
them may be artificially enhanced by some spatial processing
methods. It is hard to determine whether the improvement of
classification accuracy is caused by incorporating spatial
information into the classifier or by increasing the overlap
between training and testing samples. To partially solve this
problem, we propose a novel controlled random sampling strategy
for spectral-spatial methods. It can significantly reduce the
overlap between training and testing samples and provides more
objective and accurate evaluation
Signal processing algorithms for enhanced image fusion performance and assessment
The dissertation presents several signal processing algorithms for image fusion in noisy multimodal
conditions. It introduces a novel image fusion method which performs well for image
sets heavily corrupted by noise. As opposed to current image fusion schemes, the method has
no requirements for a priori knowledge of the noise component. The image is decomposed with
Chebyshev polynomials (CP) being used as basis functions to perform fusion at feature level. The
properties of CP, namely fast convergence and smooth approximation, renders it ideal for heuristic
and indiscriminate denoising fusion tasks. Quantitative evaluation using objective fusion assessment
methods show favourable performance of the proposed scheme compared to previous efforts
on image fusion, notably in heavily corrupted images.
The approach is further improved by incorporating the advantages of CP with a state-of-the-art
fusion technique named independent component analysis (ICA), for joint-fusion processing
based on region saliency. Whilst CP fusion is robust under severe noise conditions, it is prone to
eliminating high frequency information of the images involved, thereby limiting image sharpness.
Fusion using ICA, on the other hand, performs well in transferring edges and other salient features
of the input images into the composite output. The combination of both methods, coupled with
several mathematical morphological operations in an algorithm fusion framework, is considered a
viable solution. Again, according to the quantitative metrics the results of our proposed approach
are very encouraging as far as joint fusion and denoising are concerned.
Another focus of this dissertation is on a novel metric for image fusion evaluation that is based
on texture. The conservation of background textural details is considered important in many fusion
applications as they help define the image depth and structure, which may prove crucial in
many surveillance and remote sensing applications. Our work aims to evaluate the performance of image fusion algorithms based on their ability to retain textural details from the fusion process.
This is done by utilising the gray-level co-occurrence matrix (GLCM) model to extract second-order
statistical features for the derivation of an image textural measure, which is then used to
replace the edge-based calculations in an objective-based fusion metric. Performance evaluation
on established fusion methods verifies that the proposed metric is viable, especially for multimodal
scenarios
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