scholarly journals HYPERSPECTRAL IMAGE CLASSIFICATION BY EXPLOITING CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 535-540 ◽  
Author(s):  
B. Hosseiny ◽  
H. Rastiveis ◽  
S. Daneshtalab
Keyword(s):  
Hyperspectral Image ◽  
Training Sample ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Training Sample Size ◽  
Sensing Applications ◽  
Processing Step ◽  
Remote Sensing Applications ◽  
Spectral Dimensionality ◽  
Learning Concept

Abstract. High spectral dimensionality of hyperspectral images makes them useful data resources for earth observation in many remote sensing applications. In this case, the convolutional neural network (CNN) can help to extract deep and robust features from hyperspectral images. The main goal of this paper is to use deep learning concept to extract deep features from hyperspectral datasets to achieve better classification results. In this study, after pre-processing step, data is fed to a CNN in order to extract deep features. Extracted features are then imported in a multi-layer perceptron (MLP) network as our selected classifier. Obtained classification accuracies, based on training sample size, vary from 94.3 to 97.17% and 92.35 to 98.14% for Salinas and Pavia datasets, respectively. These results expressed more than 10% improvements compared to the classic MLP classification technique.

scholarly journals A CLASS-OUTLIER APPROACH FOR ENVIRONNEMENTAL MONITORING USING UAV HYPERSPECTRAL IMAGES

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1195-1199
Author(s):  
S. Hemissi ◽  
I. Riadh Farah
Keyword(s):  
Hyperspectral Images ◽  
High Dimensional ◽  
Relative Uncertainty ◽  
Coverage Area ◽  
Data Set ◽  
Sensing Applications ◽  
Processing Step ◽  
Belief Theory ◽  
Remote Sensing Applications ◽  
Class Labels

In several remote sensing applications, detecting exceptional/irregular regions (i.e, pixels) with respect to the whole dataset homogeneity is regarded as a very interested issue. Currently, this is limited to the pre-processing step aiming to eliminate the cloud or noisy pixels. In this paper, we propose to extend the coverage area and to tackle this issue by regarding the irregular/exceptional pixels as outliers. The main purpose is the adaptation of the class outlier mining concept in order to find abnormal and irregular pixels in hyperspectral images. This should be done taking into account the class labels and the relative uncertainty of collected data. To reach this goal, the Class Outliers: DistanceBased (CODB) algorithm is enhanced to take into account the multivariate high-dimensional data and the concomitant partially available knowledge of our data. This is mainly done by using belief theory and a learnable task-specific similarity measure. To validate our approach, we apply it for vegetation inspection and normality monitoring. For experimental purposes, the Airborne Prism Experiment (APEX) data, set acquired during an APEX flight campaign in June 2011, was used. Moreover, a collection of simulated hyperspectral images and spectral indices, providing a quantitative indicator of vegetation health, were generated for this purpose. The encouraging obtained results can be used to monitor areas where vegetation may be stressed, as a proxy to detect potential drought.

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 Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4878
Author(s):  
Ivan Racetin ◽  
Andrija Krtalić
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Anomaly Detection ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Hyperspectral Image Processing ◽  
Sensing Applications ◽  
Remote Sensing Applications

Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two decades, the availability of hyperspectral data has sharply increased, propelling the development of a plethora of hyperspectral classification and target detection algorithms. Anomaly detection methods in hyperspectral images refer to a class of target detection methods that do not require any a-priori knowledge about a hyperspectral scene or target spectrum. They are unsupervised learning techniques that automatically discover rare features on hyperspectral images. This review paper is organized into two parts: part A provides a bibliographic analysis of hyperspectral image processing for anomaly detection in remote sensing applications. Development of the subject field is discussed, and key authors and journals are highlighted. In part B an overview of the topic is presented, starting from the mathematical framework for anomaly detection. The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. Specific anomaly detection methods are presented with corresponding detection statistics, and their properties are discussed. This paper represents the first review regarding hyperspectral image processing for anomaly detection in remote sensing applications.

scholarly journals VIRTUAL TRAINING SAMPLE GENERATION BY GENERATIVE ADVERSARIAL NETWORKS FOR HYPERSPECTRAL IMAGES CLASSIFICATION

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 63-69 ◽  
Author(s):  
T. Alipourfard ◽  
H. Arefi
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Training Sample ◽  
Small Sample ◽  
Hyperspectral Images ◽  
Generative Adversarial Networks ◽  
Hyperspectral Image Classification ◽  
Virtual Training ◽  
Adversarial Networks ◽  
Training Samples

Abstract. Convolutional Neural Networks (CNNs) as a well-known deep learning technique has shown a remarkable performance in visual recognition applications. However, using such networks in the area of hyperspectral image classification is a challenging and time-consuming process due to the high dimensionality and the insufficient training samples. In addition, Generative Adversarial Networks (GANs) has attracted a lot of attentions in order to generate virtual training samples. In this paper, we present a new classification framework based on integration of multi-channel CNNs and new architecture for generator and discriminator of GANs to overcome Small Sample Size (SSS) problem in hyperspectral image classification. Further, in order to reduce the computational cost, the methods related to the reduction of subspace dimension were proposed to obtain the dominant feature around the training sample to generate meaningful training samples from the original one. The proposed framework overcomes SSS and overfitting problem in classifying hyperspectral images. Based on the experimental results on real and well-known hyperspectral benchmark images, our proposed strategy improves the performance compared to standard CNNs and conventional data augmentation strategy. The overall classification accuracy in Pavia University and Indian Pines datasets was 99.8% and 94.9%, respectively.

scholarly journals Approaches for Hyperspectral Image Classification Detailed Review

2021 ◽  
Vol 11 (1) ◽  
pp. 13-22
Author(s):  
Kushalatha M R ◽  
◽  
Prasantha H S ◽  
Beena R. Shetty ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Hyperspectral Image Classification ◽  
Sensing Applications ◽  
Remote Sensing Applications

Hyperspectral Image (HSI) processing is the new advancement in image / signal processing field. The growth over the years is appreciable. The main reason behind the successful growth of the Hyperspectral imaging field is due to the enormous amount of spectral and spatial information that the imagery contains. The spectral band that the HSI which contains is also more in number. When an image is captured through the HSI cameras, it contains around 200-250 images of the same scene. Nowadays HSI is used extensively in the fields of environmental monitoring, Crop-Field monitoring, Classification, Identification, Remote sensing applications, Surveillance etc. The spectral and spatial information content present in Hyperspectral images are with high resolutions.Hyperspectral imaging has shown significant growth and widely used in most of the remote sensing applications due to its presence of information of a scene over hundreds of contiguous bands In. Hyperspectral Image Classification of materials is the critical application of HSI using Hyperspectral sensors. It collects hundreds of spectrum channels, where each channel consists of a sharp point of Electromagnetic Spectrum. The paper mainly focuses on Deep Learning techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector machines (SVM), K-Nearest Neighbour (KNN) for the accuracy in classification. Finally in the summary the current state-of-the-art scheme, a critical discussion after reviewing the research work by other professionals and organizing it into review-based paper, also implying about the present status on classification accuracy using neural networks is carried out.

scholarly journals Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4472
Author(s):  
Tianyu Zhang ◽  
Cuiping Shi ◽  
Diling Liao ◽  
Liguo Wang
Keyword(s):  
Image Classification ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Feature Maps ◽  
Spatial Feature ◽  
Global Context

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images.

scholarly journals Overview of Hyperspectral Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjing Lv ◽  
Xiaofei Wang
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Good Classification ◽  
Semisupervised Classification

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

scholarly journals A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

2019 ◽  
Vol 11 (17) ◽  
pp. 2057 ◽  
Author(s):  
Majid Shadman Roodposhti ◽  
Arko Lucieer ◽  
Asim Anees ◽  
Brett Bryan
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Mean Shift ◽  
Training Sample ◽  
Uncertainty Assessment ◽  
Hyperspectral Image Classification ◽  
Rule Based ◽  
Classification Framework ◽  
Rule Sets ◽  
Ensemble Algorithms

This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.

scholarly journals Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1734 ◽  
Author(s):  
Tien-Heng Hsieh ◽  
Jean-Fu Kiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Principal Components ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Agricultural Lands ◽  
Hyperspectral Image Classification ◽  
Spatial Features ◽  
Salinas Valley ◽  
Crop Agriculture

Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture in Salinas Valley and a mixed vegetation agriculture in Indian Pines were used to compare the performance of these CNN algorithms. The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which contain both spectral and spatial features embedded in the HSI data.

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