scholarly journals Clustering-Based Band Selection Using Structural Similarity Index and Entropy for Hyperspectral Image Classification

2020 ◽  
Vol 37 (5) ◽  
pp. 785-791
Author(s):  
Arsalan Ghorbanian ◽  
Yasser Maghsoudi ◽  
Ali Mohammadzadeh
Keyword(s):  
Image Classification ◽  
Clustering Algorithm ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Similarity Index ◽  
Structural Similarity ◽  
Kappa Coefficient ◽  
Hyperspectral Image Classification ◽  
Structural Similarity Index ◽  
Classification Tasks

Despite the unique capabilities of hyperspectral images for classification tasks, handling the high dimension of these data is challenging. Therefore, dimension reduction algorithms have been proposed to solve this challenge. In this paper, an unsupervised Feature Selection (FS) algorithm was proposed for hyperspectral image classification. First, the entropy values of hyperspectral bands were employed to identify and remove noisy bands. Afterward, the Structural Similarity (SSIM) index and the k-means clustering algorithm were combined to select a few representative bands. Subsequently, the selected bands were injected into a supervised classifier, and the obtained Overall Accuracy (OA) and Kappa Coefficient (KC) were used to evaluate the performance of the proposed method. Finally, the results were compared with the ones achieved from other well-known and state-of-the-art FS approaches. The results revealed that the proposed method outperformed other FS algorithms. Furthermore, the proposed FS algorithm obtained equal or higher OA and KC in comparison with the case of employing all hyperspectral bands. Additionally, a stability analysis step was performed to investigate the consistency of the proposed method. The results suggest the potential of the FS approach for hyperspectral image classification.

scholarly journals No-Reference Blurred Image Quality Assessment by Structural Similarity Index

2018 ◽  
Vol 8 (10) ◽  
pp. 2003 ◽  
Author(s):  
Haopeng Zhang ◽  
Bo Yuan ◽  
Bo Dong ◽  
Zhiguo Jiang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Similarity Index ◽  
Structural Similarity ◽  
Luminance Contrast ◽  
Structural Similarity Index ◽  
Contrast Structure ◽  
Blurred Image

No-reference (NR) image quality assessment (IQA) objectively measures the image quality consistently with subjective evaluations by using only the distorted image. In this paper, we focus on the problem of NR IQA for blurred images and propose a new no-reference structural similarity (NSSIM) metric based on re-blur theory and structural similarity index (SSIM). We extract blurriness features and define image blurriness by grayscale distribution. NSSIM scores an image quality by calculating image luminance, contrast, structure and blurriness. The proposed NSSIM metric can evaluate image quality immediately without prior training or learning. Experimental results on four popular datasets show that the proposed metric outperforms SSIM and well-matched to state-of-the-art NR IQA models. Furthermore, we apply NSSIM with known IQA approaches to blurred image restoration and demonstrate that NSSIM is statistically superior to peak signal-to-noise ratio (PSNR), SSIM and consistent with the state-of-the-art NR IQA models.

scholarly journals Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Zhen-tao Qin ◽  
Wu-nian Yang ◽  
Ru Yang ◽  
Xiang-yu Zhao ◽  
Teng-jiao Yang
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Kappa Coefficient ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Images ◽  
Sparsity Pattern ◽  
Contextual Characteristics ◽  
Linear Svm

This paper presents a new, dictionary-based method for hyperspectral image classification, which incorporates both spectral and contextual characteristics of a sample clustered to obtain a dictionary of each pixel. The resulting pixels display a common sparsity pattern in identical clustered groups. We calculated the image’s sparse coefficients using the dictionary approach, which generated the sparse representation features of the remote sensing images. The sparse coefficients are then used to classify the hyperspectral images via a linear SVM. Experiments show that our proposed method of dictionary-based, clustered sparse coefficients can create better representations of hyperspectral images, with a greater overall accuracy and a Kappa coefficient.

scholarly journals Hyperspectral Image Classification Using Deep Genome Graph-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6467
Author(s):  
Haron Tinega ◽  
Enqing Chen ◽  
Long Ma ◽  
Richard M. Mariita ◽  
Divinah Nyasaka
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Genomic Analysis ◽  
Hybrid Models ◽  
Hyperspectral Image Classification ◽  
Spatial Features ◽  
Classification Tasks ◽  
3D Cnn ◽  
Genome Graph ◽  
Better Than

Recently developed hybrid models that stack 3D with 2D CNN in their structure have enjoyed high popularity due to their appealing performance in hyperspectral image classification tasks. On the other hand, biological genome graphs have demonstrated their effectiveness in enhancing the scalability and accuracy of genomic analysis. We propose an innovative deep genome graph-based network (GGBN) for hyperspectral image classification to tap the potential of hybrid models and genome graphs. The GGBN model utilizes 3D-CNN at the bottom layers and 2D-CNNs at the top layers to process spectral–spatial features vital to enhancing the scalability and accuracy of hyperspectral image classification. To verify the effectiveness of the GGBN model, we conducted classification experiments on Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SA) datasets. Using only 5% of the labeled data for training over the SA, IP, and UP datasets, the classification accuracy of GGBN is 99.97%, 96.85%, and 99.74%, respectively, which is better than the compared state-of-the-art methods.

scholarly journals Deep Quadruplet Network for Hyperspectral Image Classification with a Small Number of Samples

2020 ◽  
Vol 12 (4) ◽  
pp. 647 ◽  
Author(s):  
Chengye Zhang ◽  
Jun Yue ◽  
Qiming Qin
Keyword(s):  
Image Classification ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Feature Space ◽  
Hyperspectral Image Classification ◽  
Time Analysis ◽  
Time Consumption ◽  
Ablation Study ◽  
Improving Accuracy

This study proposes a deep quadruplet network (DQN) for hyperspectral image classification given the limitation of having a small number of samples. A quadruplet network is designed, which makes use of a new quadruplet loss function in order to learn a feature space where the distances between samples from the same class are shortened, while those from a different class are enlarged. A deep 3-D convolutional neural network (CNN) with characteristics of both dense convolution and dilated convolution is then employed and embedded in the quadruplet network to extract spatial-spectral features. Finally, the nearest neighbor (NN) classifier is used to accomplish the classification in the learned feature space. The results show that the proposed network can learn a feature space and is able to undertake hyperspectral image classification using only a limited number of samples. The main highlights of the study include: (1) The proposed approach was found to have high overall accuracy and can be classified as state-of-the-art; (2) Results of the ablation study suggest that all the modules of the proposed approach are effective in improving accuracy and that the proposed quadruplet loss contributes the most; (3) Time-analysis shows the proposed methodology has a similar level of time consumption as compared with existing methods.

scholarly journals Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification

2019 ◽  
Vol 11 (11) ◽  
pp. 1307 ◽  
Author(s):  
Wenping Ma ◽  
Qifan Yang ◽  
Yue Wu ◽  
Wei Zhao ◽  
Xiangrong Zhang
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Hyperspectral Image Classification ◽  
Spatial Feature ◽  
Types Of Information ◽  
Different Characteristics ◽  
Better Than

Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

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