Texture analysis of AIRSAR images for land cover classification

Putra, Syaza Putri Abdul Rahman; Keat, Sim Chong; Abdullah, K.; San, Lim Hwee; Adlan, Mohd Nordin

doi:10.1109/iconspace.2011.6015892

Cited by 6 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effectiveness of using image texture features for remote sensing data classification has been justified by several studies [39][40][41]. Similar to [42], the following image texture features are calculated in this work:…”

Section: Image Texture Featuresmentioning

confidence: 99%

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

Sun

Lin

Shen

et al. 2017

IJGI

View full text Add to dashboard Cite

Abstract:As an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classifying high-resolution remote sensing data. Specifically, the presented method proceeds in three main stages: feature extraction, classification, and classified result refinement. In the feature extraction stage, both multispectral images and 3D geometry data are used, which utilizes the complementary information from multisource data. In the classification stage, to tackle the problems associated with too many training samples and take full advantage of the information in the large-scale dataset, a random forest (RF) ensemble learning strategy is proposed by combining several RF classifiers together. Finally, an improved fully connected conditional random field (FCCRF) graph model is employed to derive the contextual information to refine the classification results. Experiments on the ISPRS Semantic Labeling Contest dataset show that the presented 3-stage method achieves 86.9% overall accuracy, which is a new state-of-the-art non-CNN (convolutional neural networks)-based classification method.

show abstract

Section: Image Texture Featuresmentioning

confidence: 99%

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

Sun

Lin

Shen

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

“…In addition to polarimetric information in PolSAR image, spatial structure information as an important feature should also be considered in classification. Commonly, numerous spatial feature extraction methods were proposed [20]- [25]. Generally, polarimetric features and spatial features are extracted independently and separately, which does not combine the polarimetric and spatial information of adjacent pixels in PolSAR image simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Joint Polarimetric-Adjacent Features Based on LCSR for PolSAR Image Classification

Wang

Zhang

Wang³

et al. 2021

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

View full text Add to dashboard Cite

Image classification is a critical and important application in PolSAR image interpretation. Finding a feature extraction method, which can effectively describe the characteristics of the target, is an important basis for image classification. In addition to unique polarimetric features of PolSAR system, spatial adjacent features of image also need to be considered. So in this paper, a joint polarimetric-adjacent features extraction method based on Local Convolution Sparse Representation (LCSR) is proposed for PolSAR image classification. Firstly, this paper uses Convolutional Sparse Representation (CSR) to achieve the convolution of the image filters and the feature responses so as to achieve the effective combination of the polarimetric and adjacent information of the image. Meanwhile, construct and train the dictionary using local strategy in the original domain to avoid the high computational complexity and the confusion of different grounds caused by global dictionary. Finally, SVM is used to combine the extracted features to achieve the classification. Three sets of full polarimetric data are used and the experiment results prove that the proposed method can effectively combine the polarimetric and adjacent information of data and have a good performance in PolSAR image classification.

show abstract

“…Other than that, images are sometimes unavailable for certain places and time besides exposed to other crucial issues, particularly cloud and haze effects [2]. Later, in the 1990s, besides satellite-based remote sensing technology, there were efforts to mount imaging systems on aircraft due to the need to capture images with higher spatial and temporal resolution as such onboard NASA Dryden DC-8 aircraft during the AirSAR PacRIM campaign; however, the operational costs were very expensive due to involving aircraft system maintenance [3]. Beginning 2000s, Unmanned Air Vehicles (UAVs) have been used to overcome the issues of using satellite-and aircraft-based technology; however, UAVs were initially massive and expensive in which the owners were normally among big organisations.…”

Section: Introductionmentioning

confidence: 99%

Noise and Restoration of UAV Remote Sensing Images

Ahmad¹,

Amira²,

Mawardy³

et al. 2020

IJACSA

View full text Add to dashboard Cite

Remotely sensed images captured from a camera mounted on a UAV (unmanned aerial vehicle) are exposed to noise caused by internal factors, such as the UAV system itself or external factors such as atmospheric conditions. Such images need to be restored before they can undergo further processing stages. This study aims to analyse the effects of salt and pepper noise on a UAV image and restore the image by removing the noise effects. In doing so, a UAV image, with red, green and blue channel and containing regions of different spectral properties, is experimented with salt and pepper noise of different densities. Image restoration procedure is formulated using median filtering of variable sizes. Peak-signal to noise ratio (PSNR) and mean square error (MSE) analysis are performed to measure image quality before and after restoration. An optimal filter size is chosen based on the highest PSNR of the restored image. The results show that the effects of noise on UAV images are dependent on the spectral properties of the image channels and the regions of interest. The proposed restoration works best for images with low-compared to high-density noises. Blue channel is found having the largest variation of optimal filter size, 18.5, compared to other channels because of the high response to noise within its short spectral wavelength region. Landscape's vegetation has the largest variation of optimal filter size, 22, compared to other regions due to the sensitivity of its dark spectral properties.

show abstract

Texture analysis of AIRSAR images for land cover classification

Cited by 6 publications

References 3 publications

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

Joint Polarimetric-Adjacent Features Based on LCSR for PolSAR Image Classification

Noise and Restoration of UAV Remote Sensing Images

Contact Info

Product

Resources

About