Hyperspectral classification<i>via</i>deep networks and superpixel segmentation

Liu, Yazhou; Cao, Guo; Sun, Quansen; Siegel, Mel

doi:10.1080/01431161.2015.1055607

Cited by 69 publications

(32 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arguably, the most successful of these learned feature extraction methods for remote sensing imagery is the stacked autoencoder [27,28,29,30,31]. An autoencoder is an unsupervised neural network that learns an efficient encoding of the training data.…”

Section: Deep-learning For Non-rgb Sensorsmentioning

confidence: 99%

Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning

Kemker

Salvaggio

Kanan

2018

ISPRS Journal of Photogrammetry and Remote Sensing

445

191

View full text Add to dashboard Cite

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated synthetic MSI in order to initialize a DCNN framework. We evaluate our network initialization scheme on the new RIT-18 dataset that we present in this paper. This dataset contains very-high resolution MSI collected by an unmanned aircraft system. The models initialized with synthetic imagery were less prone to over-fitting and provide a state-of-the-art baseline for future work.

show abstract

Section: Deep-learning For Non-rgb Sensorsmentioning

confidence: 99%

Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning

Kemker

Salvaggio

Kanan

2018

ISPRS Journal of Photogrammetry and Remote Sensing

445

191

View full text Add to dashboard Cite

show abstract

“…With the development of imaging technology, hyperspectral remote sensing has become one of the most important directions in the field of remote sensing. Because of their rich spectral information, hyperspectral images have been widely used in environmental monitoring, precision agriculture, smart city, information defense, resource management and other fields [1][2][3]. Hyperspectral classification is an important research branch of hyperspectral image processing, which assigns each pixel its corresponding ground category label [4].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification

et al. 2020

View full text Add to dashboard Cite

In recent years, deep learning technology has been widely used in the field of hyperspectral image classification and achieved good performance. However, deep learning networks need a large amount of training samples, which conflicts with the limited labeled samples of hyperspectral images. Traditional deep networks usually construct each pixel as a subject, ignoring the integrity of the hyperspectral data and the methods based on feature extraction are likely to lose the edge information which plays a crucial role in the pixel-level classification. To overcome the limit of annotation samples, we propose a new three-channel image build method (virtual RGB image) by which the trained networks on natural images are used to extract the spatial features. Through the trained network, the hyperspectral data are disposed as a whole. Meanwhile, we propose a multiscale feature fusion method to combine both the detailed and semantic characteristics, thus promoting the accuracy of classification. Experiments show that the proposed method can achieve ideal results better than the state-of-art methods. In addition, the virtual RGB image can be extended to other hyperspectral processing methods that need to use three-channel images.

show abstract

“…The overall accuracy and comparison with some recent research are shown in Table 4. [15] 98.02 -MLRsub [5] 92.5 -HA-PSO-SVM [17] 98.2 -SdA [7] 91.9 95.5…”

Section: Classification Accuracy and Hardware Occupationmentioning

confidence: 99%

A Scalable Dataflow Accelerator for Real Time Onboard Hyperspectral Image Classification

Wang

Niu

et al. 2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Real-time hyperspectral image classification is a necessary primitive in many remotely sensed image analysis applications. Previous work has shown that Support Vector Machines (SVMs) can achieve high classification accuracy, but unfortunately it is very computationally expensive.This paper presents a scalable dataflow accelerator on FPGA for real-time SVM classification of hyperspectral images.To address data dependencies, we adapt multi-class classifier based on Hamming distance. The architecture is scalable to high problem dimensionality and available hardware resources. Implementation results show that the FPGA design achieves speedups of 26x, 1335x, 66x and 14x compared with implementations on ZYNQ, ARM, DSP and Xeon processors. Moreover, one to two orders of magnitude reduction in power consumption is achieved for the AVRIS hyperspectral image datasets.

show abstract

Hyperspectral classificationviadeep networks and superpixel segmentation

Cited by 69 publications

References 46 publications

Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning

Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning

Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification

A Scalable Dataflow Accelerator for Real Time Onboard Hyperspectral Image Classification

Contact Info

Product

Resources

About