A hybrid OSVM-OCNN Method for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery

Abstract

Accurate information on crop distribution is of great importance for a range of applications including crop yield estimation, greenhouse gas emission measurement and management policy formulation. Fine spatial resolution (FSR) remotely sensed imagery provides new opportunities for crop mapping at a detailed level. However, crop classification from FSR imagery is known to be challenging due to the great intra-class variability and low inter-class disparity in the data. In this research, a novel hybrid method (OSVM-OCNN) was proposed for crop classification from FSR imagery, which combines a shallow-structured object-based support vector machine (OSVM) with a deep-structured object-based convolutional neural network (OCNN). Unlike pixel-wise classification methods, the OSVM-OCNN method operates on objects as the basic units of analysis and, thus, classifies remotely sensed images at the object level. The proposed OSVM-OCNN harvests the complementary characteristics of the two sub-models, the OSVM with effective extraction of low-level within-object features and the OCNN with capture and utilization of high-level between-object information. By using a rule-based fusion strategy based primarily on the OCNN’s prediction probability, the two sub-models were fused in a concise and effective manner. We investigated the effectiveness of the proposed method over two test sites (i.e., S1 and S2) that have distinctive and heterogeneous patterns of different crops in the Sacramento Valley, California, using FSR Synthetic Aperture Radar (SAR) and FSR multispectral data, respectively. Experimental results illustrated that the new proposed OSVM-OCNN approach increased markedly the classification accuracy for most of crop types in S1 and all crop types in S2, and it consistently achieved the most accurate accuracy in comparison with its two object-based sub-models (OSVM and OCNN) as well as the pixel-wise SVM (PSVM) and CNN (PCNN) methods. Our findings, thus, suggest that the proposed method is as an effective and efficient approach to solve the challenging problem of crop classification using FSR imagery (including from different remotely sensed platforms). More importantly, the OSVM-OCNN method is readily generalisable to other landscape classes and, thus, should provide a general solution to solve the complex FSR image classification problem.

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

“…CNN-Polygon also resulted in the highest performance when the sample size was less than 200 for Gwangju. This implies that the graph-based CNNs can yield successful classification results with a small training sample size, unlike recent CNN-based land cover classification studies that used large datasets with hundreds to thousands of training samples per class [18,19,31,36,64]. As the sample size increased, the performance of matrix-based models (i.e., CNN-Matrix and CNN-1D) increased to similar or slightly higher levels than the graph image-based models.…”

“…Various CNN architectures have been developed and utilized, including fully convolutional network [20][21][22][23][24][25], U-Net [26,27], modified U-Net [28], and TreeUNet [29]. CNNs have also integrated with other algorithms, such as multilayer perceptrons [30] and support vector machines [31]. Many studies have reported that CNNs have contributed to an accuracy improvement of land cover classification, with the overall accuracy ranging from 81% to 93%, depending on the sensor type, spatial resolution of input images, and target classes [18,19,21,27,[29][30][31][32][33].Feature engineering is defined as the process of transforming raw data into features for better representation of the given problem, which can result in an improvement of the model accuracy on unseen data [34].…”

“…CNNs have also integrated with other algorithms, such as multilayer perceptrons [30] and support vector machines [31]. Many studies have reported that CNNs have contributed to an accuracy improvement of land cover classification, with the overall accuracy ranging from 81% to 93%, depending on the sensor type, spatial resolution of input images, and target classes [18,19,21,27,[29][30][31][32][33].Feature engineering is defined as the process of transforming raw data into features for better representation of the given problem, which can result in an improvement of the model accuracy on unseen data [34]. Good features are a contributing factor in model performance since machine learning algorithms are problem specific and dependent on their domains.…”

Deep Learning Driven Crop Classification and Chlorophyll Content Estimation for the Nexus Food higher Productions using Multi-spectral Remote Sensing Images

“…Multi-spectral satellite images enable classification and recognition of crops, it takes into account the variations in reflectance as a function of the specific yield types [6]. Crop classification discovers applications in checking and planning efficient crop cultivation, soil and water quality studies, and land usage.…”

An Adaptive Capsule Network for Hyperspectral Remote Sensing Classification

“…Due to its outperformance and robustness, CNN has been widely used in diverse fields such as object recognition, image classification, and speech recognition [7]. In the field of remote sensing, CNN has also been widely used for the accurate classification of crop, urban land use, wetland, and so on [8][9][10][11]. For the classification of hyperspectral remote sensing, CNN-based methods including 1D, 2D, 3D-CNN, multiscale CNN, residual CNN, and object-based CNN have been proposed [12][13][14][15].…”