Improved Active Deep Learning for Semi-Supervised Classification of Hyperspectral Image

Wang, Qingyan; Chen, Meng; Zhang, Junping; Kang, Shouqiang; Wang, Yujing

doi:10.3390/rs14010171

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…Wang et al [ 24 ] suggested a semi-supervised HSI classification model which improved deep learning. Here, the suggested model namely the arbitrary multiple graphs method, and then replaced skilled learning with the anchor graph method that could be labelled a significant unlabelled data automatically and precisely.…”

Section: Literature Reviewmentioning

confidence: 99%

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Reddy¹,

Harikiran²,

Enduri

et al. 2022

Computational Intelligence and Neuroscience

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The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction. This research analyzes how to accurately classify new HSI from limited samples with labels. A novel deep-learning-based categorization based on feature extraction and classification is designed for this purpose. Initial extraction of spectral and spatial information is followed by spectral and spatial information integration to generate fused features. The classification challenge is completed using a compressed synergic deep convolution neural network with Aquila optimization (CSDCNN-AO) model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO). The HSI, the Kennedy Space Center (KSC), the Indian Pines (IP) dataset, the Houston U (HU) dataset, and the Salinas Scene (SS) dataset are used for experiment assessment. The sequence testing on these four HSI-classified datasets demonstrate that our innovative framework outperforms the conventional technique on common evaluation measures such as average accuracy (AA), overall accuracy (OA), and Kappa coefficient (k). In addition, it significantly reduces training time and computational cost, resulting in enhanced training stability, maximum performance, and remarkable training accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Reddy¹,

Harikiran²,

Enduri

et al. 2022

Computational Intelligence and Neuroscience

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“…The main goal of such systems is to perform selected input queries against a large number of classifiers. When compared to random sampling, sample selection utilizing active learning (AL) [46] is more discriminative. The suggested work is based on the presence of a zone of uncertainty among a collection of training samples.…”

Section: Active Learningmentioning

confidence: 99%

Multi-Class Pixel Certainty Active Learning Model for Classification of Land Cover Classes Using Hyperspectral Imagery

et al. 2022

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An accurate identification of objects from the acquisition system depends on the clear segmentation and classification of remote sensing images. With the limited financial resources and the high intra-class variations, the earlier proposed algorithms failed to handle the sub-optimal dataset. The building of an efficient training set iteratively in active learning (AL) approaches improves classification performance. The heuristics-based AL provides better results with the inheritance of contextual information and the robustness to noise variations. The uncertainty exists pixel variations make the heuristics-based AL fail to handle the remote sensing image classification. Previously, we focused on the extraction of clear textural pattern information by using the extended differential pattern-based relevance vector machine (EDP-AL). This paper extends that work into the novel pixel-certainty activity learning (PCAL) based on the information about textural patterns obtained from the extended differential pattern (EDP). Initially, distributed intensity filtering (DIF) is used to eliminate noise from the image, and then histogram equalization (HE) is used to improve the image quality. The EDP is used to merge and classify different labels for each image sample, and this algorithm expresses the textural information. The PCAL technique is used to classify the HSI patterns that are important in remote sensing applications using this pattern collection. Pavia University and Indian Pines (IP) are the datasets used to validate the performance of the proposed PCAL (PU). The ability of PCAL to accurately categorize land cover types is demonstrated by a comparison of the proposed PCAL with existing algorithms in terms of classification accuracy and the Kappa coefficient.

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“…Classification of hyperspectral images involves identifying various forms of information on Earth's surface using hyperspectral satellite data (Wang et al, 2021). This is achieved through the classification of satellite images, utilizing machine learning and deep learning techniques to identify areas containing various types of living organisms and objects (He et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Using Improved Active Deep Learning Framework Including Edge Preserving Filters

DHEYAA AL-SAMMARRAİE,

KARACA

2024

Turk J Remote Sens GIS

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To extract valuable information from satellite data for applications such as agriculture, geological research, and environmental monitoring, the classification of hyperspectral images is an essential task. Labeling each pixel in this process is time-consuming and requires financial resources. To this end, working with a small number of samples is very important. In order to provide high classification performances with a limited number of samples, this paper aims to enhance the performance with an active learning framework. The framework incorporates dimensionality reduction, an edge-preserving filter, and active learning steps. From this perspective, we investigated different edge-preserving filter methods to analyze the effects on performance. By combining edge-preserving filters with dimensionality reduction, the study presents a unique method that improves classification performance while maintaining image quality and reducing noise. The following five edge-preserving smoothing filters are evaluated: weighted least squares (WLS), Joint-Histogram weighted median filter (Joint WMF), fast global image smoother (FGS), bilateral filter (BF), and static/dynamic (SD). Our experiments demonstrate that compared to the reference research (CNN+AL+MRF), the proposed framework increased overall and average accuracies about 2-5% for Indian Pines, Pavia University, and Salinas datasets.

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Improved Active Deep Learning for Semi-Supervised Classification of Hyperspectral Image

Cited by 10 publications

References 39 publications

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Multi-Class Pixel Certainty Active Learning Model for Classification of Land Cover Classes Using Hyperspectral Imagery

Hyperspectral Image Classification Using Improved Active Deep Learning Framework Including Edge Preserving Filters

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