Mapping Tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks

Abstract

Tea is an important economic plant, which is widely cultivated in many countries, particularly in China. Accurately mapping tea plantations is crucial in the operations, management, and supervision of the growth and development of the tea industry. We propose an object-based convolutional neural network (CNN) to extract tea plantations from very high resolution remote sensing images. Image segmentation was performed to obtain image objects, while a fine-tuned CNN model was used to extract deep image features. We conducted feature selection based on the Gini index to reduce the dimensionality of deep features, and the selected features were then used for classifying tea objects via a random forest. The proposed method was first applied to Google Earth images and then transferred to GF-2 satellite images. We compared the proposed classification with existing methods: Object-based classification using random forest, Mask R-CNN, and object-based CNN without fine-tuning. The results show the proposed method achieved a higher classification accuracy than other methods and produced smaller over- and under-classification geometric errors than Mask R-CNN in terms of shape integrity and boundary consistency. The proposed approach, trained using Google Earth images, achieved comparable results when transferring to the classification of tea objects from GF-2 images. We conclude that the proposed method is effective for mapping tea plantations using very high-resolution remote sensing images even with limited training samples and has huge potential for mapping tea plantations in large areas.

Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis

“…The proposed system will be very helpful to many industries and public transport authorities, including bridges on the pathway. To assess the effectiveness of the proposed technique, it was compared with state-of-the-art techniques such as Auto-CAE [ 9 ], ResNet-50 [ 26 ], Crack Hessian [ 29 ] and Seg + SVM [ 30 ] and the results are tabulated in Table 5 .…”

“…ResNet-50 is a variant of ResNet with 50 neural network layers [ 26 ] as shown in Figure 6 , redrawn from [ 39 ]. Over the years, the higher accuracy and efficiency of neural network models have been achieved by deepening the neural network model, i.e., adding more layers and blocks or changing the filter size.…”

“…In [ 24 ], to inspect the severity of the crack, the authors used crack magnifier. The deep learning models such as VGG-16, ResNet50 and Inception ResNet-V2 are discussed in [ 25 , 26 , 27 ]. Paramanandham et al [ 28 ] discussed about concrete crack detection using various deep learnbing models.…”

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

“…The WOA algorithm optimizes the hyperparameters required to train the DeepCNN classifier. The backbone network used to develop the WOA-based DeepCNN classifier is ResNet-50 [65] . The WOA-based DeepCNN classifier contains one zeropadding2D layer of size 3 × 3, a convolutional-2D layer of size 7 × 7, BN, ReLu, and a max-pooling-2D layer.…”

Application of Convolutional Neural Networks With Object-Based Image Analysis for Land Cover and Land Use Mapping in Coastal Areas: A Case Study in Ain Témouchent, Algeria

“…Similarly, through the convolutional layers, the CNN model can extract features based on multiple convolutional operations in an input image, thereby transforming a local receptive field of the connected region on the input data into a pixel of the next layer. Furthermore, the pooling layer is important in any CNN model which merges similar features into one, capable of reducing feature map dimensions [36], [37]. Average and max pooling are typically the most applied layers in CNNs.…”