1,180 research outputs found
Machine Learning for Classifying Marine Vegetation from Hyperspectral Drone Data in the Norwegian coast
Along the Norwegian coasts the presence of blue forests are the key marine habitats. Due to increased anthropogenic activity and climate change, the health and extent of the blue forests is threatened. However, no low-cost, reliable system for monitoring blue forests exists in Norway at this time. This thesis studied machine learning methods to classify marine vegetation from hyperspectral data acquired in Norway. The study area is situated by Larvik at Ølbergholmen. The dataset consists of 12 hyperspectral images with 173 spectral bands in the region 390 nm - 749 nm and corresponding labels of the different classes. This dataset was used to train and evaluate the machine learning methods. In addition, an independent dataset from a different site was used for robustness evaluation. Three machine learning methods were studied; Random Forest (RF), Support Vector Machines (SVM) and Convolutional Neural Network (CNN). The results indicate that the powerful CNN approach had the best performance during validation based on the computed statistical measures. However, when evaluated for robustness, RF performed the best. The computed confusion matrices for the validation and robustness studies revealed that the presence of a so-called turf algae caused difficulties in distinguishing between the classes, which is an important finding with regard to future research. This thesis has shown that machine learning can be used for monitoring blue forests and various marine vegetation species using hyperspectral drone imaging along the Norwegian coast
A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has led to the
development of new techniques for digital pattern classification. Very recently, deep learning (DL)
models have emerged as a powerful solution to approach many machine learning (ML) problems.
In particular, convolutional neural networks (CNNs) are currently the state of the art for many image
classification tasks. While there exist several promising proposals on the application of CNNs to
LULC classification, the validation framework proposed for the comparison of different methods
could be improved with the use of a standard validation procedure for ML based on cross-validation
and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed
architecture and parametrization, to achieve high accuracy on LULC classification over RS data
from different sources such as radar and hyperspectral. We also present a methodology to perform
a rigorous experimental comparison between our proposed DL method and other ML algorithms
such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out
demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance
for all the datasets studied, regardless of their different characteristics.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
Change detection is one of the central problems in earth observation and was
extensively investigated over recent decades. In this paper, we propose a novel
recurrent convolutional neural network (ReCNN) architecture, which is trained
to learn a joint spectral-spatial-temporal feature representation in a unified
framework for change detection in multispectral images. To this end, we bring
together a convolutional neural network (CNN) and a recurrent neural network
(RNN) into one end-to-end network. The former is able to generate rich
spectral-spatial feature representations, while the latter effectively analyzes
temporal dependency in bi-temporal images. In comparison with previous
approaches to change detection, the proposed network architecture possesses
three distinctive properties: 1) It is end-to-end trainable, in contrast to
most existing methods whose components are separately trained or computed; 2)
it naturally harnesses spatial information that has been proven to be
beneficial to change detection task; 3) it is capable of adaptively learning
the temporal dependency between multitemporal images, unlike most of algorithms
that use fairly simple operation like image differencing or stacking. As far as
we know, this is the first time that a recurrent convolutional network
architecture has been proposed for multitemporal remote sensing image analysis.
The proposed network is validated on real multispectral data sets. Both visual
and quantitative analysis of experimental results demonstrates competitive
performance in the proposed mode
A Comprehensive Review on Intelligent Techniques in Crop Pests and Diseases
Artificial intelligence (AI) has transformative potential in the agricultural sector, particularly in managing and preventing crop diseases and pest infestations. This review discusses the significance of early detection and precise diagnosis of various AI tools and techniques for disease identification, such as image processing, machine learning, and deep learning. It also addresses the challenges of AI implementation in agriculture, including data quality, costs, and ethical concerns. The analysis classifies the hurdles and AI offers benefits such as improved resource management, timely interventions, and enhanced productivity. Collaborative efforts are essential to harness AI's potential for sustainable and resilient agriculture
Very High Resolution (VHR) Satellite Imagery: Processing and Applications
Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing
Semantic integration of geospatial concepts - a study on land use land cover classification systems
In GI Science, one of the most important interoperability is needed in land use and land cover (LULC) data, because it is key to the evaluation of LULC's many environmental impacts throughout the globe (Foley et al. 2005). Accordingly, this research aims to address the interoperability of LULC information derived by different authorities using different classificatory approaches.
LULC data are described by LULC classification systems. The interoperability of LULC data hinges on the semantic integration of LULC classification systems. Existing works on semantically integrating LULC classification systems has a major drawback in finding comparable semantic representations from textual descriptions. To tackle this problem, we borrowed the method of comparing documents in information retrieval, and applied it to comparing LULC category names and descriptions. The results showed significant improvement comparing to previous works.
However, lexical semantic methods are not able to solve the semantic heterogeneities in LULC classification systems: the confounding conflict - LULC categories under similar labels and descriptions have different LULC status in reality, and the naming conflict - LULC categories under different labels represent similar LULC type. Without confirmation of their actual land cover status from remote sensing, lexical semantic method cannot achieve reliable matching.
To discover confounding conflicts and reconcile naming conflicts, we developed an innovative method of applying remote sensing to the integration of LULC classification systems. Remote sensing is a means of observation on actual LULC status of individual parcels. We calculated parcel level statistics from spectral and textural data, and used these statistics to calculate category similarity. The matching results showed this approach fulfilled its goal - to overcome semantic heterogeneities and achieve more reliable and accurate matching between LULC classifications in the majority of cases.
To overcome the limitations of either method, we combined the two by aggregating their output similarities, and achieve better integration. LULC categories that post noticeable differences between lexical semantics and remote sensing once again remind us of semantic heterogeneities in LULC classification systems that must to be overcome before LULC data from different sources become interoperable and serve as the key to understanding our highly interrelated Earth system
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