Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data

Kwan, Chiman; Gribben, David; Ayhan, Bulent; Bernabé, Sergio; Selva, Massimo

doi:10.3390/rs12091392

Cited by 27 publications

(24 citation statements)

References 43 publications

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“…In JSR, a 3 × 3 or 5 × 5 patch of pixels is used in the S target matrix. The parameter s 0 in Equation (13) of [52] is the design parameter, which controls the number of sparse elements. Details of the mathematics have been described by the authors of [52].…”

Section: Comparison With Conventional Methodsmentioning

confidence: 99%

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Deep Learning for Land Cover Classification Using Only a Few Bands

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There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.

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Section: Comparison With Conventional Methodsmentioning

confidence: 99%

“…In addition to these two conventional methods, classification results reported by two papers, which used this same dataset with hyperspectral and LiDAR bands augmented with EMAP, were also considered for comparison as well. Details of the JSR and SVM results have been described by the authors of [52].…”

Section: Comparison With Conventional Methodsmentioning

confidence: 99%

Deep Learning for Land Cover Classification Using Only a Few Bands

et al. 2020

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“…In many land cover classification papers, researchers use all of the multispectral and hyperspectral bands for classification. Recently, there have been some investigations [17][18][19] that only use a few bands such as RGB and NIR bands, and yet can still achieve reasonable classification accuracy. In [17], it was found that synthetic bands using Extended Multi-attribute Profiles (EMAP) [20][21][22][23] can help improve the land classification performance quite significantly.…”

Section: Of 29mentioning

confidence: 99%

An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

et al. 2020

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Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

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“…This is shown to illustrate how low performing some types are even among the high-performing methods. Table 3, extracted from a recent work [34], corresponds to the accuracy for the full 15 class models while Table 4 is for consolidated 5 classes. Comparing the two tables, it can be seen that the new class combination results in much improved results in all cases.…”

Section: Consolidation Of the Number Of Land Cover Classesmentioning

confidence: 99%

Practical Digital Terrain Model Extraction Using Image Inpainting Techniques

Kwan¹,

Gribben²,

Ayhan³

et al. 2020

Recent Advances in Image Restoration With Applications to Real World Problems

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In some applications such as construction planning and land surveying, an accurate digital terrain model (DTM) is essential. However, in urban and sub-urban areas, the terrain may be covered by trees and man-made structures. Although digital surface model (DSM) obtained by radar or LiDAR can provide a general idea of the terrain, the presence of trees, buildings, etc. conceals the actual terrain elevation. Normally, the process of extracting DTM involves a land cover classification followed by a trimming step that removes the elevation due to trees and buildings. In this chapter, we assume the land cover types have been classified and we focus on the use of image inpainting algorithms for DTM generation. That is, for buildings and trees, we remove those pixels from the DSM and then apply inpainting techniques to reconstruct the terrain pixels in those areas. A dataset with DSM and hyperspectral data near the U. Houston area was used in our study. The DTM from United States Geological Survey (USGS) was used as the ground truth. Objective evaluation results indicate that some inpainting methods perform better than others.

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Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data

Cited by 27 publications

References 43 publications

Deep Learning for Land Cover Classification Using Only a Few Bands

Deep Learning for Land Cover Classification Using Only a Few Bands

An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Practical Digital Terrain Model Extraction Using Image Inpainting Techniques

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