Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery

Guo, Alan J. X.; Zhu, Fei

doi:10.48550/arxiv.1711.07141

Cited by 1 publication

(12 citation statements)

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“…Recently, the neural network-based models have been utilized in hyperspectral images classification, achieving remarkable improvements over the traditional methods in terms of classification performance. Earlier works include the stacked autoencoder (SAE) [24,25], the deep belief network (DBN) [26] and etc.. More recently, many researches are dedicated to the varieties of CNN and RNN-based models, as studied in [27][28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

“…Training with pixel patches is a natural idea to take advantage of both spectral and spatial information, and is adopted by most of the aforementioned neural network-based studies [27][28][29][30][31][32][33][34][35]37]. Representative works include the so-called 3D-CNN in [28], where pixel patches are directly fed to the deep model, and the integrated spectral-spatial features can be extracted from the hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, to circumvent the obstacle raised by training with pixel patches, frameworks are built by concatenating a spectrum-based model and a post spatial information integration scheme. Following this idea, the pixel-pair feature algorithm (PPF) [38] and the ANNC with adaptive spatial-spectral center classifier (ANNC-ASSCC) [35] have been proposed in the literature. Together with the aforementioned MASR and 3D-CNN, the four methods are chosen to be compared with the proposed CSFF algorithm in the experiments, as to be reported in Section 3.…”

Section: Introductionmentioning

confidence: 99%

“…In [35], an ANN-based feature extraction and classification framework is proposed. The so-called ANNC-ASSCC algorithm consists of two successive steps:…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we choose the ANNC network in [35] as the spectral feature extraction model, and design a CNN-based Spacial Feature Fusion (CSFF) algorithm to posteriorly integrate the spatial information. The main characters of the proposed classification framework are listed as below.…”

Section: Introductionmentioning

confidence: 99%

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A CNN-Based Spatial Feature Fusion Algorithm for Hyperspectral Imagery Classification

Guo

Zhu

2019

IEEE Trans. Geosci. Remote Sensing

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The shortage of training samples remains one of the main obstacles in applying the neural networks to the hyperspectral images classification. To fuse the spatial and spectral information, pixel patches are often utilized to train a model, which may further aggregate this problem. In the existing works, an ANN model supervised by center-loss (ANNC) was introduced. Training merely with spectral information, the ANNC yields discriminative spectral features suitable for the subsequent classification tasks. In this paper, we propose a novel CNN-based spatial feature fusion (CSFF) algorithm which allows a smart integration of spatial information to the spectral features extracted by ANNC. As a critical part of CSFF, a CNN-based discriminant model is introduced to estimate whether two pixels belong to the same class. At the testing stage, by applying the discriminant model to the pixel pairs generated by a test pixel and each of its neighbors, the local structure is estimated and represented as a customized convolutional kernel. The spectral-spatial feature is generated by a convolutional operation between the estimated kernel and the corresponding spectral features within a local region. The final label is determined by classifying the resulting spectral-spatial feature. Without increasing the number of training samples or involving pixel patches at the training stage, the CSFF framework achieves the state-of-the-art by declining 20% − 50% classification failures in experiments on three well-known hyperspectral images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In [35], an ANN-based feature extraction and classification framework is proposed. The so-called ANNC-ASSCC algorithm consists of two successive steps:…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations