scholarly journals TCANet for Domain Adaptation of Hyperspectral Images

2019 ◽  
Vol 11 (19) ◽  
pp. 2289 ◽  
Author(s):  
Alberto S. S. Garea ◽  
Dora B. Heras ◽  
Francisco Argüello
Keyword(s):  
Remote Sensing ◽  
Input Data ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Domain Adaptation ◽  
Computational Cost ◽  
Hyperspectral Images ◽  
Classification Problems ◽  
Output Stage ◽  
The Difference

The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid this problem, a deep learning network for DA in remote sensing hyperspectral images called TCANet is proposed. As a standard CNN, TCANet consists of several stages built based on convolutional filters that operate on patches of the hyperspectral image. Unlike the former, the coefficients of the filter are obtained through Transfer Component Analysis (TCA). This approach has two advantages: firstly, TCANet does not require training based on backpropagation, since TCA is itself a learning method that obtains the filter coefficients directly from the input data. Second, DA is performed on the fly since TCA, in addition to performing dimensional reduction, obtains components that minimize the difference in distributions of data in the different domains corresponding to the source and target images. To build an operating scheme, TCANet includes an initial stage that exploits the spatial information by providing patches around each sample as input data to the network. An output stage performing feature extraction that introduces sufficient invariance and robustness in the final features is also included. Since TCA is sensitive to normalization, to reduce the difference between source and target domains, a previous unsupervised domain shift minimization algorithm consisting of applying conditional correlation alignment (CCA) is conditionally applied. The results of a classification scheme based on CCA and TCANet show that the DA technique proposed outperforms other more complex DA techniques.

scholarly journals Hyperspectral Image Classification Based on Spectral and Spatial Information Using Multi-Scale ResNet

2019 ◽  
Vol 9 (22) ◽  
pp. 4890 ◽  
Author(s):  
Zong-Yue Wang ◽  
Qi-Ming Xia ◽  
Jing-Wen Yan ◽  
Shu-Qi Xuan ◽  
Jin-He Su ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Computational Cost ◽  
Data Cube ◽  
Hyperspectral Image Classification ◽  
Fixed Size ◽  
Multi Scale

Hyperspectral imaging (HSI) contains abundant spectrums as well as spatial information, providing a great basis for classification in the field of remote sensing. In this paper, to make full use of HSI information, we combined spectral and spatial information into a two-dimension image in a particular order by extracting a data cube and unfolding it. Prior to the step of combining, principle component analysis (PCA) is utilized to decrease the dimensions of HSI so as to reduce computational cost. Moreover, the classification block used during the experiment is a convolutional neural network (CNN). Instead of using traditionally fixed-size kernels in CNN, we leverage a multi-scale kernel in the first convolutional layer so that it can scale to the receptive field. To attain higher classification accuracy with deeper layers, residual blocks are also applied to the network. Extensive experiments on the datasets from Pavia University and Salinas demonstrate that the proposed method significantly improves the accuracy in HSI classification.

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.

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 Vegetation Height Estimation using Satellite Remote Sensing in Peat Land of Central Kalimantan

2021 ◽  
Vol 6 (1) ◽  
pp. 024-034
Author(s):  
Atriyon Julzarika ◽  
Harintaka Harintaka ◽  
Tatik Kartika
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Geoid Undulation ◽  
Height Estimation ◽  
Central Kalimantan ◽  
Vegetation Height ◽  
Accuracy Test ◽  
Sensing Technology ◽  
The Difference ◽  
Difference Test

Vegetation height is an important parameter in monitoring peatlands. Vegetation height can be estimated using remote sensing. Vegetation height can be estimated by utilizing DSM and DTM. The data that can be used are LiDAR, X-SAR, and SRTM C. In this study, LiDAR data is used for DSM2018 and DTM2018 extraction. The purpose of this research is to detect the vegetation height in Central Kalimantan peatlands using remote sensing technology. The research location is in Bakengbongkei, Kalampangan, Central Kalimantan. The integration of X-SAR and SRTM C is used for DSM2000 and DTM2000 extraction. DSM2000, DTM2000, DSM2018, and DTM2018 performed height error correction with tolerance of 1.96? (95%). Then do the geoid undulation correction to EGM2008. The results obtained are DSM and DTM with a similar height reference field. If it meets these conditions it can be calculated the vegetation height estimation. Vegetation height can be obtained using the Differential DEM method. The Changing in vegetation height from 2000 to 2018 can be estimated from the difference in vegetation height from 2000 to vegetation height in 2018. Results of spatial information on vegetation height and its changes need to be tested for the accuracy. This accuracy-test includes a cross section test, height difference test, and comparison with measurements of vegetation height in the field. The results of this research can be used to monitor the changing the vegetation height in peatlands.

scholarly journals A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images

2020 ◽  
Vol 12 (18) ◽  
pp. 3005
Author(s):  
Maofan Zhao ◽  
Qingyan Meng ◽  
Linlin Zhang ◽  
Die Hu ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computational Cost ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Segmentation Evaluation ◽  
Parameters Selection ◽  
Object Based ◽  
Geographic Object ◽  
The Difference

The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.

scholarly journals Approximate Sparse Regularized Hyperspectral Unmixing

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Chengzhi Deng ◽  
Yaning Zhang ◽  
Shengqian Wang ◽  
Shaoquan Zhang ◽  
Wei Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optimization Model ◽  
Optimization Problem ◽  
Augmented Lagrangian ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Experimental Results ◽  
Hyperspectral Images ◽  
Hyperspectral Unmixing ◽  
Variable Splitting

Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser thanl1regularizer and much easier to be solved thanl0regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison tol1regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-artl1regularizer.

scholarly journals Hyperspectral Image Classification Based on Sparse Superpixel Graph

2021 ◽  
Vol 13 (18) ◽  
pp. 3592
Author(s):  
Yifei Zhao ◽  
Fengqin Yan
Keyword(s):  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Hyperspectral Image Classification ◽  
Computational Burden ◽  
Large Size ◽  
High Classification Accuracy ◽  
Good Classification

Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.

scholarly journals A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 263
Author(s):  
Amal Altamimi ◽  
Belgacem Ben Ben Youssef
Keyword(s):  
Remote Sensing ◽  
Systematic Review ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Sensor Technology ◽  
Future Research ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
The Impact

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

scholarly journals Coded Aperture Hyperspectral Image Reconstruction

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6551
Author(s):  
Ignacio García-Sánchez ◽  
Óscar Fresnedo ◽  
José P. González-Coma ◽  
Luis Castedo
Keyword(s):  
Image Reconstruction ◽  
Hyperspectral Image ◽  
Computational Cost ◽  
Hyperspectral Images ◽  
Coded Aperture ◽  
Block Model ◽  
Reconstruction Procedure ◽  
Estimation Algorithms ◽  
Efficient Mechanisms

In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms: OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective.

scholarly journals Hyperspectral Image Classification Based on Fusion of Curvature Filter and Domain Transform Recursive Filter

2019 ◽  
Vol 11 (7) ◽  
pp. 833 ◽  
Author(s):  
Jianshang Liao ◽  
Liguo Wang
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Recursive Filter ◽  
Texture Information ◽  
Margin Distribution ◽  
Correlation Information

In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method based on the fusion of two spatial information, which will be classified by a large margin distribution machine (LDM). First, the spatial texture information is extracted from the top of the principal component analysis for hyperspectral images by a curvature filter (CF). Second, the spatial correlation information of a hyperspectral image is completed by using domain transform recursive filter (DTRF). Last, the spatial texture information and correlation information are fused to be classified with LDM. The experimental results of hyperspectral images classification demonstrate that the proposed curvature filter and domain transform recursive filter with LDM(CFDTRF-LDM) method is superior to other classification methods.

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