scholarly journals Spectral-spatial detection of buildings with special roofing in hyperspectral images

2021 ◽  
Author(s):  
Davood Akbari
Keyword(s):  
Spatial Information ◽  
High Spatial Resolution ◽  
Image Data ◽  
Angle Measurement ◽  
Hyperspectral Images ◽  
Spectral Image ◽  
Spatial Properties ◽  
Recent Developments ◽  
Minimum Spanning Forest ◽  
Spatial Detection

Abstract One of the analyses performed on hyperspectral images is target detection. Given the recent developments and the creation of images with high spatial resolution, the need for both use of spectral and spatial information in the detection of hyperspectral images has increased. The present research was conducted to introduce a new method for spectral-spatial detection of hyperspectral images. In the proposed method, the spectral image was primarily segmented using the watershed algorithm. Afterwards, for the objects resulting from segmentation, five spatial properties of area, perimeter, strength, meaning intensity, and entropy were extracted. Finally, the detection operation was performed utilizing the marker-based minimum spanning forest (MSF) algorithm. The above-mentioned techniques were applied to two sets of CASI sensor image data taken from the urban area of ​​Toulouse in southern France. The results of quantitative and qualitative evaluations showed that the proposed method improved the kappa coefficient by 40% and 34% in comparison with the spectral angle measurement (SAM) algorithm in the two tested images.

scholarly journals Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels

TecnoLógicas ◽  
2019 ◽  
Vol 22 (46) ◽  
pp. 1-14 ◽  
Author(s):  
Jorge Luis Bacca ◽  
Henry Arguello
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Subspace Clustering ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Image Clustering ◽  
Spectral Signature ◽  
Spectral Image ◽  
Sparse Subspace Clustering

Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces.  Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.

scholarly journals A NEW SPECTRAL-SPATIAL FRAMEWORK FOR CLASSIFICATION OF HYPERSPECTRAL DATA

2017 ◽  
Vol IV-4/W4 ◽  
pp. 43-46
Author(s):  
D. Akbari
Keyword(s):  
Spatial Information ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Land Cover Mapping ◽  
Binary Classifier ◽  
Accuracy Class ◽  
Acceptable Accuracy ◽  
Svm Algorithm ◽  
Minimum Spanning Forest

In this paper, an innovative framework, based on both spectral and spatial information, is proposed. The objective is to improve the classification of hyperspectral images for high resolution land cover mapping. The spatial information is obtained by a marker-based Minimum Spanning Forest (MSF) algorithm. A pixel-based SVM algorithm is first used to classify the image. Then, the marker-based MSF spectral-spatial algorithm is applied to improve the accuracy for classes with low accuracy. The marker-based MSF algorithm is used as a binary classifier. These two classes are the low accuracy class and the remaining classes. Finally, the SVM algorithm is trained for classes with acceptable accuracy. To evaluate the proposed approach, the Berlin hyperspectral dataset is tested. Experimental results demonstrate the superiority of the proposed method compared to the original MSF-based approach. It achieves approximately 5 % higher rates in kappa coefficients of agreement, in comparison to the original MSF-based method.

scholarly journals A NEW SPECTRAL-SPATIAL FRAMEWORK FOR CLASSIFICATION OF HYPERSPECTRAL DATA

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 7-10
Author(s):  
D. Akbari
Keyword(s):  
Spatial Information ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Land Cover Mapping ◽  
Binary Classifier ◽  
Accuracy Class ◽  
Acceptable Accuracy ◽  
Svm Algorithm ◽  
Minimum Spanning Forest

In this paper, an innovative framework, based on both spectral and spatial information, is proposed. The objective is to improve the classification of hyperspectral images for high resolution land cover mapping. The spatial information is obtained by a marker-based Minimum Spanning Forest (MSF) algorithm. A pixel-based SVM algorithm is first used to classify the image. Then, the marker- based MSF spectral-spatial algorithm is applied to improve the accuracy for classes with low accuracy. The marker-based MSF algorithm is used as a binary classifier. These two classes are the low accuracy class and the remaining classes. Finally, the SVM algorithm is trained for classes with acceptable accuracy. To evaluate the proposed approach, the Berlin hyperspectral dataset is tested. Experimental results demonstrate the superiority of the proposed method compared to the original MSF-based approach. It achieves approximately 5 % higher rates in kappa coefficients of agreement, in comparison to the original MSF-based method.

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

Using spatial information for evaluating the quality of prediction maps from hyperspectral images: A geostatistical approach

2019 ◽  
Vol 1077 ◽  
pp. 116-128 ◽  
Author(s):  
Ana Herrero-Langreo ◽  
Nathalie Gorretta ◽  
Bruno Tisseyre ◽  
Aoife Gowen ◽  
Jun-Li Xu ◽  
...  
Keyword(s):  
Spatial Information ◽  
Hyperspectral Images ◽  
Geostatistical Approach

Effects of Single Wavelength Selection for Anisakid Roundworm Larvae Detection through Multispectral Imaging

2007 ◽  
Vol 70 (8) ◽  
pp. 1890-1895 ◽  
Author(s):  
SVEIN K. STORMO ◽  
AGNAR H. SIVERTSEN ◽  
KARSTEN HEIA ◽  
HEIDI NILSEN ◽  
EDEL ELVEVOLL
Keyword(s):  
Light Scattering ◽  
Multispectral Imaging ◽  
Spatial Information ◽  
Imaging Spectroscopy ◽  
Fish Muscle ◽  
Alternative Methods ◽  
Spectral Image ◽  
Nematode Parasites ◽  
Food Quality And Safety ◽  
Commercially Important

The occurrence of parasites in fillets of commercially important fish species affects both food quality and safety. Presently, the detection and removal of nematode parasites is done by inspection on a light table (candling) and manual trimming of the fillets. This operation is costly and time-consuming and is not effective for detecting and removing all the nematodes in the fillets. In the last decades, several alternative methods have been proposed, but these methods have failed to replace the candling method. A newly described method called imaging spectroscopy has produced promising results because the operator can record both spectral and spatial information from an object. In this work, we studied single-wavelength bands from a spectral image. Discrimination between nematodes and other objects in the fillets is dependent on the level of contrast. Quantification of the contrast in such images revealed that the level of contrast varied when different wavelengths were selected, and these variations are correlated with the absorption properties of the nematode. Visible light scatters greatly in fish muscle, generally complicating the detection of nematodes. In this study, light scattering was used in a way that reduces the background complexity in spectral images. When light scattering properties were used in a wavelength range different from the bulk of the nematode light absorption, spectral images with significantly higher contrast were produced.

scholarly journals Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1827 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi ◽  
Youkyung Han ◽  
Yongil Kim
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Spatial Information ◽  
Short Term Memory ◽  
Hyperspectral Images ◽  
Convolutional Network ◽  
Ground Truth Data ◽  
Fully Convolutional Network ◽  
Training Samples ◽  
Multi Temporal

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

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