Patch-Free Bilateral Network for Hyperspectral Image Classification Using Limited Samples

Liu, Bing; Yu, Xi

doi:10.1109/jstars.2021.3121334

Cited by 10 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The vast majority of methods used for HIC-SS are built on CNN [24][25][26][27][28][29][30][31][32][33]. Among these CNN-based approaches, some focus on extracting features with better differentiability from a limited samples [24,[34][35][36], while others focus on optimizing the training process of classifiers [37]. The learning paradigms used cover a variety of models including self-supervised learning [38], transfer learning [39], active learning [40,41], meta-learning [37,42,43], and so on.…”

Section: Introductionmentioning

confidence: 99%

Advances in Hyperspectral Image Classification Methods with Small Samples: A Review

Wang,

Liu,

Chi

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Hyperspectral image (HSI) classification is one of the hotspots in remote sensing, and many methods have been continuously proposed in recent years. However, it is still challenging to achieve high accuracy classification in applications. One of the main reasons is the lack of labeled data. Due to the limitation of spatial resolution, manual labeling of HSI data is time-consuming and costly, so it is difficult to obtain a large amount of labeled data. In such a situation, many researchers turn their attention to the study of HSI classification with small samples. Focusing on this topic, this paper provides a systematic review of the research progress in recent years. Specifically, this paper contains three aspects. First, considering that the taxonomy used in previous review articles is not well-developed and confuses the reader, we propose a novel taxonomy based on the form of data utilization. This taxonomy provides a more accurate and comprehensive framework for categorizing the various approaches. Then, using the proposed taxonomy as a guideline, we analyze and summarize the existing methods, especially the latest research results (both deep and non-deep models) that were not included in the previous reviews, so that readers can understand the latest progress more clearly. Finally, we conduct several sets of experiments and present our opinions on current problems and future directions.

show abstract

Section: Introductionmentioning

confidence: 99%

Advances in Hyperspectral Image Classification Methods with Small Samples: A Review

Wang,

Liu,

Chi

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Hyperspectral image (HSIs) classification is widely used in earth observation systems [1,2]. However, the sparsity of labeled samples has long restricted the development of HSI classification technologies and results in that only a few labeled samples can be used for training models [3,4]. In this case, HSI classification directly using classifiers such as nearest neighbor, random forest (RF) and support vector machine (SVM) often could not obtain high-precision results [5].…”

Section: Introductionmentioning

confidence: 99%

ES2FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images

Liu

Gao

et al. 2022

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Classification with a few labeled samples has always been a longstanding problem in the field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample characteristics of HSI classification, a novel ensemble self-supervised feature-learning (ES2FL) method is proposed in this paper. The proposed method can automatically learn deep features conducive to classification without any annotation information, significantly reducing the dependence of deep-learning models on massive labeled samples. Firstly, to utilize the spatial–spectral information in HSIs more fully and effectively, EfficientNet-B0 is introduced and used as the backbone to model input samples. Then, through constraining the cross-correlation matrix of different distortions of the same sample to the identity matrix, the designed model can extract the latent features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. In addition, two ensemble learning strategies, feature-level and view-level ensemble, are proposed to further improve the feature-learning ability and classification performance by jointly utilizing spatial contextual information at different scales and feature information at different bands. Finally, the concatenations of the learned features and the original spectral vectors are inputted into classifiers such as random forest or support vector machine to complete label prediction. Extensive experiments on three widely used HSI data sets show that the proposed ES2FL method can learn more discriminant deep features and achieve better classification performance than existing advanced methods in the case of small samples.

show abstract

A multi-scale residual capsule network for hyperspectral image classification with small training samples

Shi

Zeng

Ren

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Patch-Free Bilateral Network for Hyperspectral Image Classification Using Limited Samples

Cited by 10 publications

References 63 publications

Advances in Hyperspectral Image Classification Methods with Small Samples: A Review

Advances in Hyperspectral Image Classification Methods with Small Samples: A Review

ES2FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images

A multi-scale residual capsule network for hyperspectral image classification with small training samples

Contact Info

Product

Resources

About