• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.25-36
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• 2014

Multispectral image classification of individual tree species is often difficult because of the spectral similarity among species. In this study, we attempted to analyze the suitability of hyperspectral image to classify coniferous tree species. Several image sets and classification methods were applied and the classification results were compared with the ones from multispectral image. Two airborne hyperspectral images (AISA, CASI) were obtained over the study area in the Gwangneung National Forest. For the comparison, ETM+ multispectral image was simulated using hyperspectral images as to have lower spectral resolution. We also used the transformed hyperspectral data to reduce the data volume for the classification. Three supervised classification schemes (SAM, SVM, MLC) were applied to thirteen image sets. In overall, hyperspectral image provides higher accuracies than multispectral image to discriminate coniferous species. AISA-dual image, which include additional SWIR spectral bands, shows the best result as compared with other hyperspectral images that include only visible and NIR bands. Furthermore, MNF transformed hyperspectral image provided higher classification accuracies than the full-band and other band reduced data. Among three classifiers, MLC showed higher classification accuracy than SAM and SVM classifiers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.231-240
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• 2017

This paper aims to study a method to estimate precise carbon absorption by quantification of forest information that uses accurate LiDAR data, hyperspectral image. To estimate precise carbon absorption value by using spatial data, a problem was found out of carbon absorption value estimation method with statistical method, which is already existed method, and then offered optimized carbon absorption estimation method with spatial information by analyzing with methods of compare digital aerial photogrammetry and LiDAR data. It turned out possible Precise classification and quantification in case of using LiDAR and hyperspectral image. Various classification of tree species was possible with use of LiDAR and hyperspectral image. Classification of hyperspectral image was matched in general with field survey and Mahalanobis distance classification method. Precise forest resources could be extracted using high density LiDAR data. Compared with existing method, 19.7% in forest area, 19.2% in total carbon absorption, 0.9% in absorption per unit area of difference created, and improvement was found out to be estimated precisely in international code.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.1
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• pp.91-97
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• 2016

The objective of this study was to classify between radish and Chinese cabbage in autumn using hyperspectral images. The hyperspectral images were acquired by Compact Airborne Spectrographic Imager (CASI) with 1m spatial resolution and 48 bands covering the visible and near infrared portions of the solar spectrum from 370 to 1044 nm with a bandwidth of 14 nm. An object-based technique is used for classification of radish and Chinese cabbage. It was found that the optimum parameter values for image segmentation were scale 400, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5. As a result, the overall accuracy of classification was 90.7 % and the kappa coefficient was 0.71. The hyperspectral images can be used to classify other crops with higher accuracy than radish and Chines cabbage because of their similar characteristic and growth time.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.221-224
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• 2005

Hyperspectral image data have great potential to depict more detailed information on biophysical characteristics of surface materials, which are not usually available with multispectral data. This study aims to test the potential of hyperspectral data for classifying five soil classes defined by the vector product interim terrain data (VITD). In this study, we try to classify surface materials of bare soil over the study area in Korea using both hyperspectral and multispectral image data. Training and test samples for classification are selected with using VITD vector map. The spectral angle mapper (SAM) method is applied to the EO-I Hyperion data and Landsat ETM+ data, that has been radiometrically corrected and geo-rectified. Higher classification accuracy is obtained with the hyperspectral data for classifying five soil classes of gravel, evaporites, inorganic silt and sand.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.205-213
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• 2018

Hyperspectral image is more increasing spectral resolution that Multi-spectral image. Because of that, each pixel of the hyperspectral image includes much more information and it is considered the most appropriate technic for land cover classification. but recent research of hyperspectral image is stayed land cover classification of general level. therefore we classified land cover of detail level using ED, SAM, SSS method and made Decision Tree from result of that. As a result, the overall accuracy of general level was improved by 1.68% and the overall accuracy of detail level was improved by 5.56%.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.959-971
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• 2019

CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.155-160
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• 2014

This paper deals with comparison of classification accuracy between three land cover classification results having difference in resolution and they were classified with eight classes including building, road, forest, etc. Airborne hyperspectral image used in this study was acquired at 1000m, 2000m, 3000m elevation and had 24 bands(0.5m spatial resolution), 48 bands(1.0m), 96 bands(1.5m). Assessment of classification accuracy showed that the classification using 48 bands hyperspectral image had outstanding result as compared with other images. For using hyperspectral image, it was verified that 1m spatial resolution image having 48 bands was appropriate to classify land cover and qualitative improvement is expected in thematic map creation using airborne hyperspectral image.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.239-251
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• 2016

Hyperspectral image is widely used for land cover classification because it has a number of narrow bands and allow each pixel to include much more information in comparison with previous multi-spectral image. However, Higher spectral resolution of hyperspectral image results in an increase in data volumes and a decrease in noise efficiency. SAM(Spectral Angle Mapping), a method based on vector inner product to compare spectrum distribution, is a highly valuable and popular way to analyze continuous spectrum of hyperspectral image. SAM is shown to be less accurate when it is used to analyze hyperspectral image for land cover classification using spectral library. this inaccuracy is due to the effects of atmosphere. We suggest a decision tree based method to compensate the defect and show that the method improved accuracy of land cover classification.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.