Object-Based Image Classification of Summer Crop with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.This research was partly financed by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds, the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain) and the Kearney Foundation of Soil Science (USA). The research of Peña was co-financed by the Fulbright-MEC postdoctoral program, financed by the Spanish Ministry for Science and Innovation, and by the JAEDoc Program, supported by CSIC and FEDER funds. ASTER data were available to us through a NASA EOS scientific investigator affiliation.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).Peer Reviewe

Object-Based Image Classification of Summer Crops with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification task

A Genetic Bayesian Approach for Texture-Aided Urban Land-Use/Land-Cover Classification

Urban land-use/land-cover classification is entering a new era with the increased availability of high-resolution satellite imagery and new methods such as texture analysis and artificial intelligence classifiers. Recent research demonstrated exciting improvements of using fractal dimension, lacunarity, and Moran’s I in classification but the integration of these spatial metrics has seldom been investigated. Also, previous research focuses more on developing new classifiers than improving the robust, simple, and fast maximum likelihood classifier. The goal of this dissertation research is to develop a new approach that utilizes a texture vector (fractal dimension, lacunarity, and Moran’s I), combined with a new genetic Bayesian classifier, to improve urban land-use/land-cover classification accuracy. Examples of different land-use/land-covers using post-Katrina IKONOS imagery of New Orleans were demonstrated. Because previous geometric-step and arithmetic-step implementations of the triangular prism algorithm can result in significant unutilized pixels when measuring local fractal dimension, the divisor-step method was developed and found to yield more accurate estimation. In addition, a new lacunarity estimator based on the triangular prism method and the gliding-box algorithm was developed and found better than existing gray-scale estimators for classifying land-use/land-cover from IKONOS imagery. The accuracy of fractal dimension-aided classification was less sensitive to window size than lacunarity and Moran’s I. In general, the optimal window size for the texture vector-aided approach is 27x27 to 37x37 pixels (i.e., 108x108 to 148x148 meters). As expected, a texture vector-aided approach yielded 2-16% better accuracy than individual textural index-aided approach. Compared to the per-pixel maximum likelihood classification, the proposed genetic Bayesian classifier yielded 12% accuracy improvement by optimizing prior probabilities with the genetic algorithm; whereas the integrated approach with a texture vector and the genetic Bayesian classifier significantly improved classification accuracy by 17-21%. Compared to the neural network classifier and genetic algorithm-support vector machines, the genetic Bayesian classifier was slightly less accurate but more computationally efficient and required less human supervision. This research not only develops a new approach of integrating texture analysis with artificial intelligence for classification, but also reveals a promising avenue of using advanced texture analysis and classification methods to associate socioeconomic statuses with remote sensing image textures

Applications of ISES for vegetation and land use

Remote sensing relative to applications involving vegetation cover and land use is reviewed to consider the potential benefits to the Earth Observing System (Eos) of a proposed Information Sciences Experiment System (ISES). The ISES concept has been proposed as an onboard experiment and computational resource to support advanced experiments and demonstrations in the information and earth sciences. Embedded in the concept is potential for relieving the data glut problem, enhancing capabilities to meet real-time needs of data users and in-situ researchers, and introducing emerging technology to Eos as the technology matures. These potential benefits are examined in the context of state-of-the-art research activities in image/data processing and management

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review

Bark beetle outbreaks can result in a devastating impact on forest ecosystem processes, biodiversity, forest structure and function, and economies. Accurate and timely detection of bark beetle infestations is crucial to mitigate further damage, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that rely on aerial surveys and field surveys, which are impractical over vast geographical regions. This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three key perspectives: bark beetle & host interactions, RS, and ML/DL. We parse recent literature according to bark beetle species & attack phases, host trees, study regions, imagery platforms & sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices (SVIs), ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. This review focuses on challenging early detection, discussing current challenges and potential solutions. Our literature survey suggests that the performance of current ML methods is limited (less than 80%) and depends on various factors, including imagery sensors & resolutions, acquisition dates, and employed features & algorithms/networks. A more promising result from DL networks and then the random forest (RF) algorithm highlighted the potential to detect subtle changes in visible, thermal, and short-wave infrared (SWIR) spectral regions.Comment: Under review, 33 pages, 5 figures, 8 Table

A Global Human Settlement Layer from optical high resolution imagery - Concept and first results

A general framework for processing of high and very-high resolution imagery for creating a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS-2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye-1, QuickBird-2, Ikonos-2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.JRC.G.2-Global security and crisis managemen

A Hybrid Machine Learning Technique For Feature Optimization In Object-Based Classification of Debris-Covered Glaciers

Object-based features like spectral, topographic, and textural are supportive to determine debris-covered glacier classes. The original feature space includes relevant and irrelevant features. The inclusion of all these features increases the complexity and renders the classifier’s performance. Therefore, feature space optimization is requisite for the classification process. Previous studies have shown a rigorous exercise in manually selecting the best combination of features to define the target class and proven to be a time consuming task. The present study proposed a hybrid feature selection technique to automate the selection of the best suitable features. This study aimed to reduce the classifier’s complexity and enhance the performance of the classification model. Relief-F and Pearson Correlation filter-based feature selection methods ranked features according to the relevance and filtered out irrelevant or less important features based on the defined condition. Later, the hybrid model selected the common features to get an optimal feature set. The proposed hybrid model was tested on Landsat 8 images of debris-covered glaciers in Central Karakoram Range and validated with present glacier inventories. The results showed that the classification accuracy of the proposed hybrid feature selection model with a Decision Tree classifier is 99.82%, which is better than the classification results obtained using other mapping techniques. In addition, the hybrid feature selection technique has sped up the process of classification by reducing the number of features by 77% without compromising the classification accuracy