scholarly journals Classification of Land Cover from Remote Sensing Images using Morphological Linear Contact Distributions and Rough Sets

2019 ◽  
Vol 8 (3) ◽  
pp. 676-688
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Rough Sets ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Classification Algorithm ◽  
Forest Monitoring ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Remote sensing image classification plays an essential role in computer vision and image processing to address the problems in the areas of agriculture, forest monitoring, urban development, environment protection, etc. A lot of literature is available on remote sensing image classification. But, it is still a research task even today because of the multitude of problems. RTBFCA (Rough Texture Based Features Classification Algorithm), a new classification algorithm has been proposed in this paper. This paper aims at classifying the remote sensing images into various cover types using mathematical morphology and rough sets. Morphological texture features (linear contact distributions) along with first order statistics are used to identify the pixels of various classes and the concepts of lower and upper approximations of rough sets are used for clustering the features of the pixels and then are finally classified to display the classified image. The proposed method was tested on Google Earth images and is able to classify even various crops patterns of a land cover image. The algorithm is compared with other algorithms like ”GLCM with rough sets”, ”intensity values with rough sets” and with ”linear contact distributions with rough sets”.

scholarly journals Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2984
Author(s):  
Gyanendra Prasad Joshi ◽  
Fayadh Alenezi ◽  
Gopalakrishnan Thirumoorthy ◽  
Ashit Kumar Dutta ◽  
Jinsang You
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Data Augmentation ◽  
Land Cover Classification ◽  
Remote Sensing Image ◽  
Environmental Modeling ◽  
Classification Model ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.

scholarly journals High Resolution Remote Sensing Image Classification Algorithm Based on Improved FCN

2021 ◽  
Vol 2006 (1) ◽  
pp. 012040
Author(s):  
Kong Yunbo ◽  
Fu Haojun ◽  
Yangfan ◽  
Zhouhai ◽  
Wen Na ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Classification Algorithm ◽  
Remote Sensing Image Classification

scholarly journals Remote Sensing Image Classification Algorithm Based on Texture Feature and Extreme Learning Machine

2020 ◽  
Vol 65 (2) ◽  
pp. 1385-1395
Author(s):  
Xiangchun Liu ◽  
Jing Yu ◽  
Wei Song ◽  
Xiaobing Zhao ◽  
Lizhi Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Texture Feature ◽  
Remote Sensing Image ◽  
Classification Algorithm ◽  
Remote Sensing Image Classification ◽  
Learning Machine

scholarly journals Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification

2019 ◽  
Vol 11 (2) ◽  
pp. 174 ◽  
Author(s):  
Han Liu ◽  
Jun Li ◽  
Lin He ◽  
Yu Wang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Data ◽  
Sampling Strategy ◽  
Remote Sensing Image ◽  
Fine Tuning ◽  
Spatial Dependency ◽  
Remote Sensing Images ◽  
Training Set ◽  
Remote Sensing Image Classification

Irregular spatial dependency is one of the major characteristics of remote sensing images, which brings about challenges for classification tasks. Deep supervised models such as convolutional neural networks (CNNs) have shown great capacity for remote sensing image classification. However, they generally require a huge labeled training set for the fine tuning of a deep neural network. To handle the irregular spatial dependency of remote sensing images and mitigate the conflict between limited labeled samples and training demand, we design a superpixel-guided layer-wise embedding CNN (SLE-CNN) for remote sensing image classification, which can efficiently exploit the information from both labeled and unlabeled samples. With the superpixel-guided sampling strategy for unlabeled samples, we can achieve an automatic determination of the neighborhood covering for a spatial dependency system and thus adapting to real scenes of remote sensing images. In the designed network, two types of loss costs are combined for the training of CNN, i.e., supervised cross entropy and unsupervised reconstruction cost on both labeled and unlabeled samples, respectively. Our experimental results are conducted with three types of remote sensing data, including hyperspectral, multispectral, and synthetic aperture radar (SAR) images. The designed SLE-CNN achieves excellent classification performance in all cases with a limited labeled training set, suggesting its good potential for remote sensing image classification.

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