When Pixels Team up: Spatially Weighted Sparse Coding for Hyperspectral Image Classification

Soltani-Farani, Ali; Rabiee, Hamid R.

doi:10.1109/lgrs.2014.2328319

Cited by 26 publications

(6 citation statements)

References 21 publications

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“…Early spatial-spectral feature extractors had hyperparameters that required tuning (e.g. gray-level co-occurrence matrices [23], Gabor [24], sparse coding [25], extended morphological attribute profiles [26], etc). These hand-crafted features could fail to generalize well across multiple datasets, so they were replaced with learned features that were automatically tuned from the data itself.…”

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

432

191

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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.

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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

432

191

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“…J. Geo-Inf. 2017, 6, 177 4 of 9 usually belong to the same materials [25]. Hence, the joint sparse model considers the sparse representation of both target pixels and their eight neighborhood pixels, as shown in Figure 1.…”

Section: Joint Sparse Model Classificationmentioning

confidence: 99%

“…In this paper, we use the simultaneous orthogonal matching pursuit (SOMP) to solve Equation (8). When the sparse coefficient matrix is recovered, we can label the target pixel x by the minimal reconstructed residual, shown by Equation (9) usually belong to the same materials [25]. Hence, the joint sparse model considers the sparse representation of both target pixels and their eight neighborhood pixels, as shown in Figure 1.…”

Section: Joint Sparse Model Classificationmentioning

confidence: 99%

Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images

Luo

Ouyang

Zhang

et al. 2017

IJGI

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Abstract:Mangroves are valuable contributors to coastal ecosystems, and remote sensing is an indispensable way to obtain knowledge of the dynamics of mangrove ecosystems. Due to the similar spectral features between mangroves and other land cover types, challenges are posed since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this paper, we propose a classification method named the multi-feature joint sparse algorithm (MF-SRU), in which spectral, topographic, and textural features are integrated as the decision-making features, and sparse representation of both center pixels and their eight neighborhood pixels is proposed to represent the spatial correlation of neighboring pixels, which can make good use of the spatial correlation of adjacent pixels. Experiments are performed on Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in Southeastern China, and the results show that the proposed method can effectively improve the extraction accuracy of mangroves.

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“…In addition, local homogeneity was exploited to select landmark points for nonlinear manifold-based spectral unmixing (Bottai et al 2013). Recently, image partitioning was integrated with a reweighted λ 1 framework in a spatially weighted sparse coding (SWSC) algorithm (Soltani-Farani and Rabiee 2015).…”

Section: Abundance Estimationmentioning

confidence: 99%

A survey of methods incorporating spatial information in image classification and spectral unmixing

Wang

Shi

Diao

et al. 2016

International Journal of Remote Sensing

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Over the past decade, the incorporation of spatial information has drawn increasing attention in multispectral and hyperspectral data analysis. In particular, the property of spatial autocorrelation among pixels has shown great potential for improving understanding of remotely sensed imagery. In this paper, we provide a comprehensive review of the state-of-the-art techniques in incorporating spatial information in image classification and spectral unmixing. For image classification, spatial information is accounted for in the stages of pre-classification, sample selection, classifiers, post-classification, and accuracy assessment. With regards to spectral unmixing, spatial information is discussed in the context of endmember extraction, selection of endmember combinations, and abundance estimation. Finally, a perspective on future research directions for advancing spatial-spectral methods is offered.

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When Pixels Team up: Spatially Weighted Sparse Coding for Hyperspectral Image Classification

Cited by 26 publications

References 21 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

Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images

A survey of methods incorporating spatial information in image classification and spectral unmixing

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