scholarly journals Fusion of Multiscale Convolutional Neural Networks for Building Extraction in Very High-Resolution Images

2019 ◽  
Vol 11 (3) ◽  
pp. 227 ◽  
Author(s):  
Genyun Sun ◽  
Hui Huang ◽  
Aizhu Zhang ◽  
Feng Li ◽  
Huimin Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Decision Fusion ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Fusion Strategy ◽  
Very High

Extracting buildings from very high resolution (VHR) images has attracted much attention but is still challenging due to their large varieties in appearance and scale. Convolutional neural networks (CNNs) have shown effective and superior performance in automatically learning high-level and discriminative features in extracting buildings. However, the fixed receptive fields make conventional CNNs insufficient to tolerate large scale changes. Multiscale CNN (MCNN) is a promising structure to meet this challenge. Unfortunately, the multiscale features extracted by MCNN are always stacked and fed into one classifier, which make it difficult to recognize objects with different scales. Besides, the repeated sub-sampling processes lead to a blurred boundary of the extracted features. In this study, we proposed a novel parallel support vector mechanism (SVM)-based fusion strategy to take full use of deep features at different scales as extracted by the MCNN structure. We firstly designed a MCNN structure with different sizes of input patches and kernels, to learn multiscale deep features. After that, features at different scales were individually fed into different support vector machine (SVM) classifiers to produce rule images for pre-classification. A decision fusion strategy is then applied on the pre-classification results based on another SVM classifier. Finally, superpixels are applied to refine the boundary of the fused results using region-based maximum voting. For performance evaluation, the well-known International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam dataset was used in comparison with several state-of-the-art algorithms. Experimental results have demonstrated the superior performance of the proposed methodology in extracting complex buildings in urban districts.

A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution

2020 ◽  
Vol 247 ◽  
pp. 111912
Author(s):  
Dujuan Zhang ◽  
Yaozhong Pan ◽  
Jinshui Zhang ◽  
Tangao Hu ◽  
Jianhua Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Large Area ◽  
Very High ◽  
Cropland Mapping

scholarly journals Apple quality identification and classification by image processing based on convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yanfei Li ◽  
Xianying Feng ◽  
Yandong Liu ◽  
Xingchang Han
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Svm Classifier ◽  
Test Accuracy ◽  
Training Time ◽  
Proposed Model ◽  
Specific Complex ◽  
Occurrence Matrix ◽  
Apple Quality

AbstractThis work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on convolutional neural networks (CNN) which aimed at accurate and fast grading of apple quality. Specific, complex, and useful image characteristics for detection and classification were captured by the proposed model. Compared with existing methods, the proposed model could better learn high-order features of two adjacent layers that were not in the same channel but were very related. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. The overall accuracy of the proposed model tested using an independent 300 apple dataset was 95.33%. The results showed that the training accuracy, overall test accuracy and training time of the proposed model were better than Google Inception v3 model and traditional imaging process method based on histogram of oriented gradient (HOG), gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The proposed model has great potential in Apple’s quality detection and classification.

scholarly journals EXTRACTION OF BUILT-UP AREAS USING CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING FROM SENTINEL-2 SATELLITE IMAGES

2018 ◽  
Vol XLII-3 ◽  
pp. 79-85 ◽  
Author(s):  
V. S. Bramhe ◽  
S. K. Ghosh ◽  
P. K. Garg
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Satellite Images ◽  
Spatial Information ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Sentinel 2

With rapid globalization, the extent of built-up areas is continuously increasing. Extraction of features for classifying built-up areas that are more robust and abstract is a leading research topic from past many years. Although, various studies have been carried out where spatial information along with spectral features has been utilized to enhance the accuracy of classification. Still, these feature extraction techniques require a large number of user-specific parameters and generally application specific. On the other hand, recently introduced Deep Learning (DL) techniques requires less number of parameters to represent more abstract aspects of the data without any manual effort. Since, it is difficult to acquire high-resolution datasets for applications that require large scale monitoring of areas. Therefore, in this study Sentinel-2 image has been used for built-up areas extraction. In this work, pre-trained Convolutional Neural Networks (ConvNets) i.e. Inception v3 and VGGNet are employed for transfer learning. Since these networks are trained on generic images of ImageNet dataset which are having very different characteristics from satellite images. Therefore, weights of networks are fine-tuned using data derived from Sentinel-2 images. To compare the accuracies with existing shallow networks, two state of art classifiers i.e. Gaussian Support Vector Machine (SVM) and Back-Propagation Neural Network (BP-NN) are also implemented. Both SVM and BP-NN gives 84.31 % and 82.86 % overall accuracies respectively. Inception-v3 and VGGNet gives 89.43 % of overall accuracy using fine-tuned VGGNet and 92.10 % when using Inception-v3. The results indicate high accuracy of proposed fine-tuned ConvNets on a 4-channel Sentinel-2 dataset for built-up area extraction.

scholarly journals VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 675-682
Author(s):  
D. James ◽  
A. Collin ◽  
A. Mury ◽  
S. Costa
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Large Scale ◽  
Near Infrared ◽  
Airborne Lidar ◽  
Support Vector ◽  
Surface Model ◽  
Global Changes ◽  
Very High

Abstract. Anthropocene is featured with increasing human population and global changes that strongly affect landscapes at an unprecedented pace. As a flagship, the coastal fringe is subject to an accelerated conversion of natural areas into agricultural ones, in turn, into urban ones, generating hazardous soil artificialization. Very high resolution (VHR) technologies such as airborne LiDAR or UAV imageries are good assets to model the topography and classify the land use/land cover (LULC), helping local management. Even if their spatial resolution suits with the management scale, their extent covers a few km2, making large-scale monitoring complex and time-consuming. VHR spaceborne imagery has a great potential to address this spatial challenge given its regional acquisition. This research proposes to evaluate the capabilities of a Pleiades-1 stereo-satellite multispectral imagery (blue, green, red, BGR, and near-infrared, NIR) to both model the surface topography and classify LULC. Horizontal and vertical accuracies of the photogrammetry-driven digital surface model (DSM) attain 0.53 m and 0.65 m, respectively. Nine LULC generic classes are studied using the maximum likelihood (ML) and support vector machine (SVM) algorithms. The classification accuracy of the basic BGR (reaching 84.64 % and 76.13 % with ML and SVM, respectively) is improved by the DSM contribution (5.49 % and 2.91 % for ML and SVM, respectively), and the NIR contribution (6.78 % and 3.89 % for ML and SVM, respectively). The gain of the DSM-NIR combination totals 8.91 % and 8.40 % for ML and SVM, respectively, making the ML-based full combination the best performance (93.55 %).

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