scholarly journals CloudScout: A Deep Neural Network for On-Board Cloud Detection on Hyperspectral Images

2020 ◽  
Vol 12 (14) ◽  
pp. 2205
Author(s):  
Gianluca Giuffrida ◽  
Lorenzo Diana ◽  
Francesco de Gioia ◽  
Gionata Benelli ◽  
Gabriele Meoni ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Hyperspectral Images ◽  
Cloud Detection ◽  
Downlink Transmission ◽  
New Era ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Increasing Demand ◽  
Embedded Applications

The increasing demand for high-resolution hyperspectral images from nano and microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A possible approach to mitigate this problem consists in reducing the amount of data to transmit to ground through on-board processing of hyperspectral images. In this paper, we propose a custom Convolutional Neural Network (CNN) deployed for a nanosatellite payload to select images eligible for transmission to ground, called CloudScout. The latter is installed on the Hyperscout-2, in the frame of the Phisat-1 ESA mission, which exploits a hyperspectral camera to classify cloud-covered images and clear ones. The images transmitted to ground are those that present less than 70% of cloudiness in a frame. We train and test the network against an extracted dataset from the Sentinel-2 mission, which was appropriately pre-processed to emulate the Hyperscout-2 hyperspectral sensor. On the test set we achieve 92% of accuracy with 1% of False Positives (FP). The Phisat-1 mission will start in 2020 and will operate for about 6 months. It represents the first in-orbit demonstration of Deep Neural Network (DNN) for data processing on the edge. The innovation aspect of our work concerns not only cloud detection but in general low power, low latency, and embedded applications. Our work should enable a new era of edge applications and enhance remote sensing applications directly on-board satellite.

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 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.

Efficient dimension reduction of hyperspectral images for big data remote sensing applications

2020 ◽  
Vol 14 (03) ◽  
pp. 1
Author(s):  
Beatriz P. Garcia-Salgado ◽  
Volodymyr I. Ponomaryov ◽  
Sergiy Sadovnychiy ◽  
Rogelio Reyes-Reyes
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Dimension Reduction ◽  
Hyperspectral Images ◽  
Sensing Applications ◽  
Remote Sensing Applications

On the parallel classification system using hyperspectral images for remote sensing applications

2018 ◽  
Author(s):  
Volodymyr I. Ponomaryov ◽  
Beatriz P. Garcia-Salgado ◽  
Cesar M. A. Robles-Gonzalez ◽  
Sergiy Sadovnychiy
Keyword(s):  
Remote Sensing ◽  
Classification System ◽  
Hyperspectral Images ◽  
Sensing Applications ◽  
Remote Sensing Applications

A saliency model-oriented convolution neural network for cloud detection in remote sensing images

2021 ◽  
Vol 17 (3) ◽  
pp. 235-247
Author(s):  
Jun Zhang ◽  
Junjun Liu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Environmental Changes ◽  
Convolution Neural Network ◽  
Kernel Principal Component Analysis ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Saliency Model

Remote sensing is an indispensable technical way for monitoring earth resources and environmental changes. However, optical remote sensing images often contain a large number of cloud, especially in tropical rain forest areas, make it difficult to obtain completely cloud-free remote sensing images. Therefore, accurate cloud detection is of great research value for optical remote sensing applications. In this paper, we propose a saliency model-oriented convolution neural network for cloud detection in remote sensing images. Firstly, we adopt Kernel Principal Component Analysis (KCPA) to unsupervised pre-training the network. Secondly, small labeled samples are used to fine-tune the network structure. And, remote sensing images are performed with super-pixel approach before cloud detection to eliminate the irrelevant backgrounds and non-clouds object. Thirdly, the image blocks are input into the trained convolutional neural network (CNN) for cloud detection. Meanwhile, the segmented image will be recovered. Fourth, we fuse the detected result with the saliency map of raw image to further improve the accuracy of detection result. Experiments show that the proposed method can accurately detect cloud. Compared to other state-of-the-art cloud detection method, the new method has better robustness.

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.

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