scholarly journals Deep Learning for Hyperspectral Data Classification through Exponential Momentum Deep Convolution Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qi Yue ◽  
Caiwen Ma
Keyword(s):  
Data Classification ◽  
Classification Problem ◽  
Hyperspectral Data ◽  
Support Vector ◽  
New Classification ◽  
Spatial Features ◽  
Classification Framework ◽  
Deep Convolution Neural Network ◽  
Quantitative Validation ◽  
High Level

Classification is a hot topic in hyperspectral remote sensing community. In the last decades, numerous efforts have been concentrated on the classification problem. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on complex handcrafted features. However, it is rarely known which features are important for the problem. In this paper, a new classification skeleton based on deep machine learning is proposed for hyperspectral data. The proposed classification framework, which is composed of exponential momentum deep convolution neural network and support vector machine (SVM), can hierarchically construct high-level spectral-spatial features in an automated way. Experimental results and quantitative validation on widely used datasets showcase the potential of the developed approach for accurate hyperspectral data classification.

scholarly journals Algorithmic-Level Approximate Tensorial SVM Using High-Level Synthesis on FPGA

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 205
Author(s):  
Hamoud Younes ◽  
Ali Ibrahim ◽  
Mostafa Rizk ◽  
Maurizio Valle
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
High Level Synthesis ◽  
Support Vector ◽  
Svm Classifier ◽  
Approximate Computing ◽  
Classification Framework ◽  
Time Latency ◽  
High Level

Approximate Computing Techniques (ACT) are promising solutions towards the achievement of reduced energy, time latency and hardware size for embedded implementations of machine learning algorithms. In this paper, we present the first FPGA implementation of an approximate tensorial Support Vector Machine (SVM) classifier with algorithmic level ACTs using High-Level Synthesis (HLS). A touch modality classification framework was adopted to validate the effectiveness of the proposed implementation. When compared to exact implementation presented in the state-of-the-art, the proposed implementation achieves a reduction in power consumption by up to 49% with a speedup of 3.2×. Moreover, the hardware resources are reduced by 40% while consuming 82% less energy in classifying an input touch with an accuracy loss less than 5%.

scholarly journals A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

2019 ◽  
Vol 11 (19) ◽  
pp. 2238 ◽  
Author(s):  
Leilei Jiao ◽  
Weiwei Sun ◽  
Gang Yang ◽  
Guangbo Ren ◽  
Yinnian Liu
Keyword(s):  
Land Cover ◽  
Coastal Wetlands ◽  
Hierarchical Classification ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Land Cover Types ◽  
Multispectral Data ◽  
Classification Framework ◽  
Land Covers

Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.

scholarly journals Hyperspectral Image Classification Using Kernel Fukunaga-Koontz Transform

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Semih Dinç ◽  
Abdullah Bal
Keyword(s):  
Hyperspectral Image ◽  
Classification Problem ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Distributed Data ◽  
Classification Problems ◽  
Novel Approach ◽  
Training Stage ◽  
Improved Performance ◽  
Two Stages

This paper presents a novel approach for the hyperspectral imagery (HSI) classification problem, using Kernel Fukunaga-Koontz Transform (K-FKT). The Kernel based Fukunaga-Koontz Transform offers higher performance for classification problems due to its ability to solve nonlinear data distributions. K-FKT is realized in two stages: training and testing. In the training stage, unlike classical FKT, samples are relocated to the higher dimensional kernel space to obtain a transformation from non-linear distributed data to linear form. This provides a more efficient solution to hyperspectral data classification. The second stage, testing, is accomplished by employing the Fukunaga- Koontz Transformation operator to find out the classes of the real world hyperspectral images. In experiment section, the improved performance of HSI classification technique, K-FKT, has been tested comparing other methods such as the classical FKT and three types of support vector machines (SVMs).

scholarly journals SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification

2021 ◽  
Vol 13 (3) ◽  
pp. 464
Author(s):  
Yinghui Quan ◽  
Xian Zhong ◽  
Wei Feng ◽  
Jonathan Cheung-Wai Chan ◽  
Qiang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Fundamental Problem ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Great Success ◽  
Learning Approaches ◽  
Rotation Forest ◽  
Training Time

Conventional classification algorithms have shown great success in balanced hyperspectral data classification. However, the imbalanced class distribution is a fundamental problem of hyperspectral data, and it is regarded as one of the great challenges in classification tasks. To solve this problem, a non-ANN based deep learning, namely SMOTE-Based Weighted Deep Rotation Forest (SMOTE-WDRoF) is proposed in this paper. First, the neighboring pixels of instances are introduced as the spatial information and balanced datasets are created by using the SMOTE algorithm. Second, these datasets are fed into the WDRoF model that consists of the rotation forest and the multi-level cascaded random forests. Specifically, the rotation forest is used to generate rotation feature vectors, which are input into the subsequent cascade forest. Furthermore, the output probability of each level and the original data are stacked as the dataset of the next level. And the sample weights are automatically adjusted according to the dynamic weight function constructed by the classification results of each level. Compared with the traditional deep learning approaches, the proposed method consumes much less training time. The experimental results on four public hyperspectral data demonstrate that the proposed method can get better performance than support vector machine, random forest, rotation forest, SMOTE combined rotation forest, convolutional neural network, and rotation-based deep forest in multiclass imbalance learning.

scholarly journals A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6378
Author(s):  
Ahmad M. Karim ◽  
Hilal Kaya ◽  
Mehmet Serdar Güzel ◽  
Mehmet R. Tolun ◽  
Fatih V. Çelebi ◽  
...  
Keyword(s):  
Linear Model ◽  
Processing System ◽  
Data Classification ◽  
Pso Algorithm ◽  
Classification Problem ◽  
Classification Performance ◽  
Softmax Classifier ◽  
Public Datasets ◽  
High Level ◽  
Hidden Neurons

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.

Robust support vector method for hyperspectral data classification and knowledge discovery

2004 ◽  
Vol 42 (7) ◽  
pp. 1530-1542 ◽  
Author(s):  
G. Camps-Valls ◽  
L. Gomez-Chova ◽  
J. Calpe-Maravilla ◽  
J.D. Martin-Guerrero ◽  
E. Soria-Olivas ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Vector Method ◽  
Support Vector Method

Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains

2021 ◽  
Author(s):  
Yao Li
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Spatial Features ◽  
Wide Range ◽  
Extraction Algorithm ◽  
Angle Match ◽  
High Level

<p>There is a growing demand for constructing a complete and accurate landslide maps and inventories in a wide range, which leading explosive growth in extraction algorithm study based on remote sensing images. To the best of our knowledge, no study focused on deep learning-based methods for landslide detection on hyperspectral images.We proposes a deep learning frameworkwith constraints to detect landslides on hyperspectral image. The framework consists of two steps. First, a deep belief network is employed to extract the spectral–spatial features of a landslide. Second, we insert the high-level features and constraints into a logistic regression classifier for verifying the landslide. Experimental results demonstrated that the framework can achieve higher overall accuracy when compared to traditional hyperspectral image classification methods. The precision of the landslide detection on the whole image, obtained by the proposed method, can reach 97.91%, whereas the precision of the linear support vector machine, spectral information divergence, and spectral angle match are 94.36%, 84.50%, and 86.44%, respectively. Also, this article reveals that the high-level feature extraction system has a significant potential for landslide detection, especially in multi-source remote sensing.</p>

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