Semantic Segmentation Based Remote Sensing Data Fusion on Crops Detection

Abstract

Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.

Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA

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“…CNNs have led in the state-of-the-art feature extraction results and are considered a hot topic in the image processing and computer vision fields, which gradually overcome traditional methods (Pena et al, 2019). CNNs as kind of mature network of deep learning model is inspired from the biological multi-layer neural networks architectures, which enable them to form high-level semantic features from the existing low-level features in an image (Jin et al, 2019).…”

Shared-Space Autoencoders with Randomized Skip Connections for Building Footprint Detection with Missing Views

“…For this reason, many detection and segmentation networks employ techniques to fuse these views to combine the useful features of each. Multi-view satellite fusion methods have been successfully utilized for tasks including crop segmentation [22], target detection [18,19], and a whole host of other application areas [8,23,20].…”

“…The straightforward approach towards such a challenging setting is to pretrain a network with the view that is missing at test time, and subsequently fine-tune the network using data available both during training and testingan approach commonly employed in satellite imagery analysis [28,10,29,1]. At the same time, several approaches have been proposed on multi-view learning for satellite imagery for a variety of tasks [22,18,19]. However, these methods are not tailored for handling missing modalities at test-time, and are thus unsuitable for the specific setting under consideration.…”

Agricultural Data Fusion for SmartAgro Telemetry System

“…The advantage of data fusion of data received from multiple different sensors relies in "an improved estimate of physical phenomenon via redundant observations" [14]. The efficiency of data fusion was previously demonstrated in precision agriculture domain [15][16][17][18]. In [15], authors present the benefits on crop monitoring of 2D and 3D data fusion for a vineyard monitoring and use the results in order to classify vines in serveral classes by processing data from multiple sources (different sensors, Unmanned Aerial Vehicle (UAVs), etc).…”