A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

Comparison of Three Techniques to Identify and Count Individual Animals in Aerial Imagery

Whether a species is rare and requires protection or is overabundant and needs control, an accurate estimate of population size is essential for the development of conservation plans and management goals. Current wildlife surveys are logistically difficult, frequently biased, and time consuming. Therefore, there is a need to provide additional techniques to improve survey methods for censusing wildlife species. We examined three methods to enumerate animals in remotely sensed aerial imagery: manual photo interpretation, an unsupervised classification, and multi- image, multi-step technique. We compared the performance of the three techniques based on the probability of correctly detecting animals, the probability of under-counting animals (false positives), and the probability of over-counting animals (false negatives). Manual photo-interpretation had a high probability of detecting an animal (81% ± 24%), the lowest probability of over-counting an animal (8% ± 16%), and a relatively low probability of under-counting an animal (19% ± 24%). An unsupervised, ISODATA classification with subtraction of a background image had the highest probability of detecting an animal (82% ± 10%), a high probability of over-counting an animal (69% ± 27%) but a low probability of under-counting an animal (18% ± 18%). The multi-image, multi-step procedure incorporated more information, but had the lowest probability of detecting an animal (50% ± 26%), the highest probability of over-counting an animal (72% ± 26%), and the highest probability of under-counting an animal (50% ± 26%). Manual interpreters better discriminated between animal and non-animal features and had fewer over-counting errors (i.e., false positives) than either the unsupervised classification or the multi-image, multi-step techniques indicating that benefits of automation need to be weighed against potential losses in accuracy. Identification and counting of animals in remotely sensed imagery could provide wildlife managers with a tool to improve population estimates and aid in enumerating animals across large natural systems.

Changes in Mediterranean Coastline According to the Coastal Type and Land Cover under Climate Change:The Case of South-east Spain

Coastline changes were analyzed considering the land cover types and the analysis of the causes that have deter­mined these changes during the past decades.Through the overlapping of aerial photographs and GIS analysis,the results showed that the land surface increased with respect to the previous stage,gaining terrain to the sea,but this increment was caused by anthropogenic processes.In fact,without human pressure,the land surface beside the coastal line would have decreased,especially on the sandy beaches and coastal dunes.Therefore,the beaches are one of the most vulnerable ecosystems and geomorphological systems due to erosion and lack of sediment supply associated with the modified river courses(i.e.by the construction of reservoirs,concrete channeling,etc.),the inner land use changes,and the effects of global warming on the sea level.Climate change studies predict specific increases in the sea level along the coast.The aim of this work is to know if anthropic activity can reverse the effects of sea level rise and coast­al erosion.In fact,it has been done for decades with measures aimed to correct impacts and favour economic activity(i.e.maintaining tourism resources)and not from the environmental issues.

Geomorphological monitoring of a highly dynamic estuary using oblique aerial photographs

Bad weather in many countries limits the use of optical satellite imageries in spatial and temporal monitoring of the environment.In this paper,a series of lowaltitude oblique aerial photos taken on daily,weekly and monthly intervals were used to monitor the geomorphological changes in the upper part of the Mersey Estuary,northwestern England.This low-altitude aerial photo methodology reveals itself to be a satisfying compromise between cost,accuracy and difficulty of implementation.It offered a large amount of information on a spatial and temporal scale aiding in the understanding of channel mobility.This was an important consideration in the sitting and installation of new bridge pier foundations.This series of oblique aerial photos was used in a dynamic model to determine the migration of the ebb channel and was effective in identifying the main route of flow.Few uncertainties were encountered and the level of accuracy achieved in resolving these uncertainties in the images was in the range from 40 cm to a maximum of 1.7 m.This was compared with historical navigation charts and showed good correlation.Further applications are required to improve the quality of the data output from these images and the development of the technique.