Dimensionality Reduction Using Band Selection Technique for Kernel Based Hyperspectral Image Classification

Reshma, R.; Sowmya, V.

doi:10.1016/j.procs.2016.07.226

Cited by 24 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This dataset was taken in a flight campaign over Pavia, Northern Italy. These data have 103 spectral bands with a 610 × 340 spatial dimension with ground truth data and have 9 different classes, as shown in Figure 3 c [ 26 ]. MFO can optimize images with multiple bands by selecting very few bands, and it is compared with other state-of-the-art methods in Table 1 .…”

Section: Classification Methodsmentioning

confidence: 99%

Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization

et al. 2022

View full text Add to dashboard Cite

In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classification. MFO is inspired by the behavior of moths with respect to flames, which is the navigation method of moths in nature called transverse orientation. In MFO, a moth navigates the search space through a process called transverse orientation by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering that the Moon’s distance from the moth is considerably long. Our research tested MFO on three benchmark hyperspectral datasets—Indian Pines, University of Pavia, and Salinas. MFO produced an Overall Accuracy (OA) of 88.98%, 94.85%, and 97.17%, respectively, on the three datasets. Our experimental results indicate that MFO produces better OA and Kappa when compared to state-of-the-art band selection algorithms such as particle swarm optimization, grey wolf, cuckoo search, and genetic algorithms. The analysis results prove that the proposed approach effectively addresses the spectral band selection problem and provides a high classification accuracy.

show abstract

Section: Classification Methodsmentioning

confidence: 99%

Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The existing studies on HSI basically use open datasets such as Indian Pines and University of Pavia [28][29][30] with corresponding label files, could focus on the pixels, through pixels to do identify and classify studies, so it can contain tens of thousands of samples in an image and bring great convenience to the experiment. However, the forage HSI images were all taken on spot, and there was no corresponding label file, it is impossible to include 10 kinds of forage in an image, the number of samples was insufficient, which increased the difficulty of the experiment.…”

Section: Data Collection and Sample Expansionmentioning

confidence: 99%

Research on identification and classification of grassland forage based on deep learning and attention mechanisms

Liu

Zhao

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

Grassland is an important resource for China's economic development and the main economic source of animal husbandry. The identification and classification of grassland forage is an important part of the improvement of forage varieties and the monitoring of germplasm resources, which can fundamentally solve the problems of poor forage quality and low reproduction rate. For the problem of low accuracy of forage identification and classification, the authors put forward a new 3DSECNN model to remove the preprocessing operation and directly study the images. The authors took forage hyperspectral image (HSI) images on the field and built dataset, used 3DSECNN to train the images to improve the classification effect. The outstanding contributions of this paper are: (1) The authors took high‐precision forage HSI images in the field, established a dedicated database of forage HSIs, and expanded the datasets; (2) the process of integrating preprocessing ideas into the network and replacing the traditional method of preprocessing the data and then extracting features; (3) proposing the 3DSECNN model, which adds SENet on the basis of the traditional 3DCNN, strengthens the correlation of the spatial dimension, selects the key features for the classification by calculating the channel weight, inhibits the unimportant information, and achieves the purpose of integrating the preprocessing ideas into the network. The experimental results show the overall accuracy (OA) of 3DSECNN is 94.36%, Precision, Recall, F1‐score, Kappa, and Time also showed good levels. The experimental results prove that the 3DSECNN strengthens the correlation between image channels, enhances the performance ability of features, and provides a new method for the identification and classification research of forage.

show abstract

“…We took all the 220 bands in the feature selection step, including the twenty water absorption bands. Note that in literature these noisy water absorption bands are often excluded before the experiments [36,37]. We wanted to check whether our model was able to reject them.…”

Section: Indian Pines Hyperspectral Imagerymentioning

confidence: 99%

Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection

Ghosh¹,

Kirby²

2022

Preprint

View full text Add to dashboard Cite

We develop a sparse optimization problem for the determination of the total set of features that discriminate two or more classes. This is a sparse implementation of the centroid-encoder for nonlinear data reduction and visualization called Sparse Centroid-Encoder (SCE). We also provide a feature selection framework that first ranks each feature by its occurrence, and the optimal number of features is chosen using a validation set. The algorithm is applied to a wide variety of data sets including, single-cell biological data, high dimensional infectious disease data, hyperspectral data, image data, and speech data. We compared our method to various state-of-the-art feature selection techniques, including two neural network-based models (DFS, and LassoNet), Sparse SVM, and Random Forest. We empirically showed that SCE features produced better classification accuracy on the unseen test data, often with fewer features.

show abstract

Dimensionality Reduction Using Band Selection Technique for Kernel Based Hyperspectral Image Classification

Cited by 24 publications

References 5 publications

Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization

Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization

Research on identification and classification of grassland forage based on deep learning and attention mechanisms

Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection

Contact Info

Product

Resources

About