Classification of hyperspectral remote sensing image via rotation ...

The proposed RILBP is an enhanced texture-based classification paradigm that utilizes local binary pattern filter for some designated bands.

The color composite remotely sensed image of the initial three subsequent bands is segmented into several consistent regions by utilizing the graph-based ...

Classification of Hyperspectral Remote Sensing Image Data from ...

In this paper, an efficient hyperspectral image classification method based on rotation forest and extreme learning machine with kernel(ROF-KELM) is presented.

Classification of hyperspectral remote sensing image via rotation ...

Download Citation | Classification of hyperspectral remote sensing image via rotation-invariant local binary pattern-based weighted generalized closest ...

Hyperspectral Image Classification Based on Improved Rotation ...

Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through ...

Hyperspectral Image Classification With Rotation Random Forest ...

Experimental analysis is conducted using real hyperspectral remote sensing images to evaluate the performance of the proposed method in ...

Hyperspectral Image Classification Based on Improved Rotation ...

Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong ...

Hyperspectral Remote Sensing Image Classification Based ... - HAL

Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, and Random ...

Classification of hyperspectral remote sensing image via rotation ...

Classification of hyperspectral remote sensing image via rotation-invariant local binary pattern-based weighted generalized closest neighbor.

[PDF] Hyperspectral Remote Sensing Image Classification Based on ...

Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, ...

Hyperspectral remote sensing image classification based on rotation ...

The classification results confirmed that integration of ancillary data increased the classification accuracy in comparison to using solely spectral bands of WV ...

Hyperspectral remote sensing image classification based on tighter ...

Classification results are obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scanner and the Reflective Optics System Imaging ...

Overview of Hyperspectral Image Classification - Wiley Online Library

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread.

Classification of hyperspectral remote sensing images with support ...

This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines by understanding and assessing the ...

Spectral–Spatial Classification for Hyperspectral Data Using ...

Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields · Attached ...

Hyperspectral Image Classification with Rotation Random Forest Via ...

Experimental analysis is conducted using real hyperspectral remote sensing images to evaluate the performance of the proposed method in comparison with RF, ...

Spectral-Spatial Attention Rotation-Invariant Classification Network ...

This paper presents a spectral-spatial attention rotation-invariant classification network for the airborne hyperspectral image to address this issue.

Hyperspectral Image Classification With Rotation Random Forest ...

Finally, the results achieved at each step are fused by a majority vote. Experimental analysis is conducted using real hyperspectral remote sensing images to ...

Image classification of hyperspectral remote sensing using semi ...

A new hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented.

Joint spatial-spectral hyperspectral image classification based on ...

The hyperspectral remote sensing data acquired by the imaging sensor has a higher spectral resolution, which can provide more abundant information of the ...