A 3d-Deep CNN Based Feature Extraction and Hyperspectral Image Classification

Kanthi, Murali; Sarma, T. Hitendra; Bindu, C. Shoba

doi:10.1109/ingarss48198.2020.9358920

Cited by 51 publications

(19 citation statements)

References 8 publications

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“…In the implementation process, HSI data is reserved to three channels through PCA method, and morphological algorithm is used to expand HSI and LiDAR data to 60-band profiles and 15-band profiles, respectively. 3) Three groups of 3 × 3 × 3 3-D convolutional layers, batch normalization layers, ReLUs, and max pooling layers are used in the 3-DCNN method [51]. In addition, the spatial patch size of the input feature is set to 11 × 11 on the three datasets.…”

Section: Experimental Comparison With Competitive Methodsmentioning

confidence: 99%

Local Information Interaction Transformer for Hyperspectral and LiDAR Data Classification

Zhang

Peng

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The multisource remote sensing classification task has two main challenges. 1) How to capture hyperspectral image (HSI) and light detection and ranging (LiDAR) features cooperatively to fully mine the complementary information between data. 2) How to adaptively fuse multisource features, which should not only overcome the imbalance between HSI and LiDAR data but also avoid the generation of redundant information. The local information interaction transformer (LIIT) model proposed herein can effectively address these above issues. Specifically, multibranch feature embedding is first performed to help in the fine-grained serialization of multisource features; subsequently, a local-based multisource feature interactor (L-MSFI) is designed to explore HSI and LiDAR features together. This structure provides an information transmission environment for multibranch features and further alleviates the homogenization processing mode of the self-attention process. More importantly, a multisource feature selection module (MSTSM) is developed to dynamically fuse HSI and LiDAR features to solve the problem of insufficient fusion. Experiments were carried out on three multisource remote-sensing classification datasets, the results of which show that LIIT has more performance advantages than the state-of-the-art CNN and transformer methods.

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Section: Experimental Comparison With Competitive Methodsmentioning

confidence: 99%

Local Information Interaction Transformer for Hyperspectral and LiDAR Data Classification

Zhang

Peng

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A Here With 3 iterations of AL, ~3% improvement in accuracy over RL and satisfactory qualitative results at no additional cost to training besides training and testing a model on a large unlabelled dataset. MURALI KANTHI Et.al [4] proposes an efficient 3D-Deep Feature Extraction CNN model for the clarity of HSI , which uses spatial information spectral. Two convolution layer, two ReLU layers, and one at the very most layer are employed in the model's suggested design.…”

Section: Introductionmentioning

confidence: 99%

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

Sagari¹,

Solanki²,

Goel³

et al. 2023

IJSREM

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Abstract—oral cavity cancer also called squamous cell carcinoma is the sixth most frequent cancer overall, it is a rising public health concern that is spreading across the globe. If not detected in earlier stages, it might be severe generally, and underlying lesions induce it. Therefore, it might be difficult to locate the lesion area and if not cured in its nascent stages, it might reach stage IV cancer, called the metastatic stage. To come up with a solution, we tend to use the three- dimensional CNN processes malignancies with image data and 3DCNN structures to classify benign and aggressive oral tumors. By identifying the kind of tumor region susing various kinds of knowledge here on periodic alterations in tumor enhancers, CNN was utilized in Test with visual categorization improvement rates. Analyzed, categorized, and future study areas are identified. The datasets utilized in the review articles are covered in detail, as are the pre- processing methods, the deep learning frameworks employed, etc. It presents a comparison of the same. Index Terms—Oral Cancer, Oral lesions, Oral Cavity, 3D CNN, Pre-processing, tumors, malignant, deep-learning

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“…It provides good classification accuracy with a small training sample. In similar manner, Kanthi et al [17] introduced a 3D-CNN approach for HSI classification, that divides HSI data into 3D patches and extracts deep spectral and spatial information. This model produced relatively high classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale 3D-convolutional neural network for hyperspectral image classification

Kanthi

Sarma²,

Bindu

2022

IJEECS

Self Cite

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Deep Learning methods are state-of-the-art approaches for pixel-based hyperspectral images (HSI) classification. High classification accuracy has been achieved by extracting deep features from both spatial-spectral channels. However, the efficiency of such spatial-spectral approaches depends on the spatial dimension of each patch and there is no theoretically valid approach to find the optimum spatial dimension to be considered. It is more valid to extract spatial features by considering varying neighborhood scales in spatial dimensions. In this regard, this article proposes a deep convolutional neural network (CNN) model wherein three different multi-scale spatial-spectral patches are used to extract the features in both the spatial and spectral channels. In order to extract these potential features, the proposed deep learning architecture takes three patches various scales in spatial dimension. 3D convolution is performed on each selected patch and the process runs through entire image. The proposed is named as multi-scale three-dimensional convolutional neural network (MS-3DCNN). The efficiency of the proposed model is being verified through the experimental studies on three publicly available benchmark datasets including Pavia University, Indian Pines, and Salinas. It is empirically proved that the classification accuracy of the proposed model is improved when compared with the remaining state-of-the-art methods.

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A 3d-Deep CNN Based Feature Extraction and Hyperspectral Image Classification

Cited by 51 publications

References 8 publications

Local Information Interaction Transformer for Hyperspectral and LiDAR Data Classification

Local Information Interaction Transformer for Hyperspectral and LiDAR Data Classification

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

Multi-scale 3D-convolutional neural network for hyperspectral image classification

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