Classification of remote sensed images using random forests and deep learning framework

Piramanayagam, Sankaranarayanan; Schwartzkopf, W.; Koehler, Frederick W.; Saber, Eli

doi:10.1117/12.2243169

Cited by 21 publications

(19 citation statements)

References 7 publications

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“…By this criterion, our classification results can be considered relevant and useful for practical applications, except for the 'car' class. As also reported by other related studies [14,17,67], compared to the other classes, the 'car' class is the most difficult category to classify, and its accuracy is usually around or even lower than 50% for most hand-crafted-features-based methods. After careful analysis, we think the following reasons may account for the low classification accuracies.…”

Section: Performance Analysismentioning

confidence: 64%

“…As far as the five specific classes are concerned, our method ranks first in the "imp surf" and "tree" classes; and second in the "building", "grass" and "car" classes. The strongest competitors are IVFL (86.5%, an object-based method) and RIT [67] (86.3%, a structured path-based method). Both methods classify the data using a random forest model.…”

Section: Performance Analysismentioning

confidence: 99%

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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

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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.

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Section: Performance Analysismentioning

confidence: 64%

Section: Performance Analysismentioning

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

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“…Tschannen et al [49] introduced a structured CNNs that employed Haar wavelet-based trees for identifying the semantic category of every pixel of remote sensing image. Piramanayagam et al [50] further exploited a multi-path CNNs that support both true ortho photo and digital surface model (DSM) for land cover classification. Marcu et al [51] presented a dual path, that is VGG-Net path and AlexNet path, to learn local and global representations of aerial images.…”

Section: Related Workmentioning

confidence: 99%

“…Our networks do not need these aides but achieve competitive scores compared with these approaches. For the fine-tuned networks from the pre-trained VGG-16 model (ONE [84], DLR [76], UOA [29], RIT [50]), their performances are not always steady compared to that of the proposed DMSMR approach. Our overall accuracy varies approximately 0.1% (see Ano (Ano is available at http://ftp.ipi.uni-hannover.de/ISPRS_WGIII_website/ISPRSIII_4_Test_results/2D_labeling_vaih/ 2D_labeling_Vaih_details_Ano/index.html) and Ano2 in the ISPRS leader board.…”

Section: Evaluation On Vaihingen Datasetmentioning

confidence: 99%

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Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images

Zhang

Zhao

et al. 2017

Remote Sensing

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Semantic image segmentation has recently witnessed considerable progress by training deep convolutional neural networks (CNNs). The core issue of this technique is the limited capacity of CNNs to depict visual objects. Existing approaches tend to utilize approximate inference in a discrete domain or additional aides and do not have a global optimum guarantee. We propose the use of the multi-label manifold ranking (MR) method in solving the linear objective energy function in a continuous domain to delineate visual objects and solve these problems. We present a novel embedded single stream optimization method based on the MR model to avoid approximations without sacrificing expressive power. In addition, we propose a novel network, which we refer to as dual multi-scale manifold ranking (DMSMR) network, that combines the dilated, multi-scale strategies with the single stream MR optimization method in the deep learning architecture to further improve the performance. Experiments on high resolution images, including close-range and remote sensing datasets, demonstrate that the proposed approach can achieve competitive accuracy without additional aides in an end-to-end manner.

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Land Use Land Cover Segmentation of LISS-III Multispectral Space-Born Image Using Deep Learning

Desai

Shukla

2023

Lecture Notes in Electrical Engineering

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Classification of remote sensed images using random forests and deep learning framework

Cited by 21 publications

References 7 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

Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images

Land Use Land Cover Segmentation of LISS-III Multispectral Space-Born Image Using Deep Learning

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