Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models

In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object's orientation and on a sensor's flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotated versions of the same object class. In this work we propose a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself. By using rotating convolutions as building blocks and passing only the the values corresponding to the maximally activating orientation throughout the network in the form of orientation encoding vector fields, RotEqNet treats rotated versions of the same object with the same filter bank and therefore achieves state-of-the-art performances even when using very small architectures trained from scratch. We test RotEqNet in two challenging sub-decimeter resolution semantic labeling problems, and show that we can perform better than a standard CNN while requiring one order of magnitude less parameters

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

A family of 26 non-parametric texture descriptors based on Histograms of Equivalent Patterns (HEP) has been tested, many of them for the first time in remote sensing applications, to improve urban classification through object-based image analysis of GeoEye-1 imagery. These HEP descriptors have been compared to the widely known texture measures derived from the gray-level co-occurrence matrix (GLCM). All the five finally selected HEP descriptors (Local Binary Patterns, Improved Local Binary Patterns, Binary Gradient Contours and two different combinations of Completed Local Binary Patterns) performed faster in terms of execution time and yielded significantly better accuracy figures than GLCM features. Moreover, the HEP texture descriptors provided additional information to the basic spectral features from the GeoEye-1's bands (R, G, B, NIR, PAN) significantly improving overall accuracy values by around 3%. Conversely, and in statistic terms, strategies involving GLCM texture derivatives did not improve the classification accuracy achieved from only the spectral information. Lastly, both approaches (HEP and GLCM) showed similar behavior with regard to the training set size applied

Estimación del carbono aéreo almacenado en Ceroxylon sp. mediante análisis geoespacial en el ACP Bosque de Palmeras de la Comunidad Campesina Taulía Molinopampa, Amazonas, 2021

Los bosques almacenan carbono y las estimaciones tradicionales del carbono forestal incluyen métodos de recolección de partes vegetales y modelado matemático. Aunque el enfoque tradicional proporciona las estimaciones más precisas de las existencias de carbono, por lo general es costoso, requiere mucha mano de obra/tiempo y, a veces, es imposible debido a la escasa/nula accesibilidad. Por tanto, esta investigación estimó el carbono aéreo almacenado en Ceroxylon sp. mediante el análisis geoespacial en el Área de Conservación Privada (ACP) Bosque de Palmeras Taulía Molinopampa (Amazonas, 2021). Para lo cual, a) se determinó la cobertura y uso actual del suelo en el ACP mediante clasificación supervisada de una imagen satelital Sentinel 2A; b) se determinó el carbono aéreo almacenado en individuos de Ceroxylon sp. con base en información dasométrica y, finalmente, c) se estimó el carbono aéreo almacenado en la totalidad del bosque de Ceroxylon sp. mediante análisis espacial. Se encontró que el 64.3% (7018.87 ha) del ACP está cubierto por bosque. El análisis dasométrico de 148 individuos de Ceroxylon sp. muestra que el diámetro de altura de pecho, y la altura de fuste promedio son 36.30 cm y 18.16 m, respectivamente, y que almacenan 510.98 kg.C/individuo. De las siete parcelas de evaluación (50 x 50 m), se calculó una densidad promedio de 0.0085 palmeras/m xv 2 , y se estimaron 593596 individuos de Ceroxylon sp. en el ACP. Finalmente, se estimó 303315.26 TM de carbono aéreo almacenado en el bosque de Ceroxylon sp. en el ACP al 2021, con un almacenamiento de 43214.26 kg.C/ha

A framework for autonomous mission and guidance control of unmanned aerial vehicles based on computer vision techniques

A computação visual é uma área do conhecimento que estuda o desenvolvimento de sistemas artificiais capazes de detectar e desenvolver a percepção do meio ambiente através de informações de imagem ou dados multidimensionais. A percepção visual e a manipulação são combinadas em sistemas robóticos através de duas etapas "olhar"e depois "movimentar-se", gerando um laço de controle de feedback visual. Neste contexto, existe um interesse crescimente no uso dessas técnicas em veículos aéreos não tripulados (VANTs), também conhecidos como drones. Essas técnicas são aplicadas para posicionar o drone em modo de vôo autônomo, ou para realizar a detecção de regiões para vigilância aérea ou pontos de interesse. Os sistemas de computação visual geralmente tomam três passos em sua operação, que são: aquisição de dados em forma numérica, processamento de dados e análise de dados. A etapa de aquisição de dados é geralmente realizada por câmeras e sensores de proximidade. Após a aquisição de dados, o computador embarcado realiza o processamento de dados executando algoritmos com técnicas de medição (variáveis, índice e coeficientes), detecção (padrões, objetos ou áreas) ou monitoramento (pessoas, veículos ou animais). Os dados processados são analisados e convertidos em comandos de decisão para o controle para o sistema robótico autônomo Visando realizar a integração dos sistemas de computação visual com as diferentes plataformas de VANTs, este trabalho propõe o desenvolvimento de um framework para controle de missão e guiamento de VANTs baseado em visão computacional. O framework é responsável por gerenciar, codificar, decodificar e interpretar comandos trocados entre as controladoras de voo e os algoritmos de computação visual. Como estudo de caso, foram desenvolvidos dois algoritmos destinados à aplicação em agricultura de precisão. O primeiro algoritmo realiza o cálculo de um coeficiente de reflectância visando a aplicação auto-regulada e eficiente de agroquímicos, e o segundo realiza a identificação das linhas de plantas para realizar o guiamento dos VANTs sobre a plantação. O desempenho do framework e dos algoritmos propostos foi avaliado e comparado com o estado da arte, obtendo resultados satisfatórios na implementação no hardware embarcado.Cumputer Vision is an area of knowledge that studies the development of artificial systems capable of detecting and developing the perception of the environment through image information or multidimensional data. Nowadays, vision systems are widely integrated into robotic systems. Visual perception and manipulation are combined in two steps "look" and then "move", generating a visual feedback control loop. In this context, there is a growing interest in using computer vision techniques in unmanned aerial vehicles (UAVs), also known as drones. These techniques are applied to position the drone in autonomous flight mode, or to perform the detection of regions for aerial surveillance or points of interest. Computer vision systems generally take three steps to the operation, which are: data acquisition in numerical form, data processing and data analysis. The data acquisition step is usually performed by cameras or proximity sensors. After data acquisition, the embedded computer performs data processing by performing algorithms with measurement techniques (variables, index and coefficients), detection (patterns, objects or area) or monitoring (people, vehicles or animals). The resulting processed data is analyzed and then converted into decision commands that serve as control inputs for the autonomous robotic system In order to integrate the visual computing systems with the different UAVs platforms, this work proposes the development of a framework for mission control and guidance of UAVs based on computer vision. The framework is responsible for managing, encoding, decoding, and interpreting commands exchanged between flight controllers and visual computing algorithms. As a case study, two algorithms were developed to provide autonomy to UAVs intended for application in precision agriculture. The first algorithm performs the calculation of a reflectance coefficient used to perform the punctual, self-regulated and efficient application of agrochemicals. The second algorithm performs the identification of crop lines to perform the guidance of the UAVs on the plantation. The performance of the proposed framework and proposed algorithms was evaluated and compared with the state of the art, obtaining satisfactory results in the implementation of embedded hardware

Uma solução de baixo custo para o processamento de imagens aéreas obtidas por Veículos Aéreos Não Tripulados

Currently, unmanned aerial vehicles (UAV) are increasingly used to aid the various tasks around the world. The popularization of this equipment associated with the advancement of technology, particularly the miniaturization of processors, extend their functionalitys. In agricultural applications, these devices allow monitoring of production by capturing aerial images, for which are processed and identified areas of interest through specific software. The research proposes a low-cost solution capable of processing aerial images obtained by non-metric digital cameras coupled to UAV to identify gaps in plantations or estimate levels of environmental degradation, which can be deployed in small computers and low power consumption. Embedded systems coupled in UAV allow perform processing in real time, which contributes to a preventive diagnosis, reduces the response time and can avoid damages in the crop. The algorithm used is based on watershed, while the second algorithm uses classification techniques based on the 1-Nearest Neighbor (1-NN). Are used the embedded systems DE2i-150 and Intel Edison, both x86 architecture, and Raspberry Pi 2 of ARM architecture. Moreover, the technique 1-NN showed higher tolerance to lighting problems, however, require more processing power compared to the algorithm based on watershed. The results show that the proposed system is an efficient and relatively low-cost solution compared to traditional means of monitoring and can be coupled in a UAV to perform the processing during the flight.Atualmente, veículos aéreos não tripulados (VANT) são cada vez mais utilizados no auxílio a diversas tarefas em todo o mundo. A popularização destes equipamentos associada ao avanço da tecnologia, sobretudo a miniaturização de processadores, ampliam suas funcionalidades. Em aplicações agrícolas, estes equipamentos permitem o monitoramento da produção por meio da captação de imagens aéreas, a partir dos quais são processadas e identificadas áreas de interesse por meio de softwares específicos. A pesquisa propõe uma solução de baixo custo capaz de processar imagens aéreas obtidas por câmeras digitais não métricas acopladas a VANT para identificar falhas em plantações ou estimar níveis de degradação ambiental, os quais possam ser implantados em computadores de pequeno porte e baixo consumo, conhecido como sistemas embarcados. Plataformas embarcadas acopladas a VANT permitem realizar o processamento em tempo real, que contribui para um diagnóstico preventivo, reduz o tempo de resposta e pode evitar prejuízos na lavoura. O algoritmo inicialmente avaliado é baseado em watershed, enquanto que o segundo algoritmo proposto faz uso de técnicas de classificação baseada no 1-vizinho mais próximo (1-NN). Utilizam-se os sistemas embarcados DE2i-150 e Intel Edison, ambos de arquitetura x86, e a plataforma Raspberry Pi 2 de arquitetura ARM. Em relação ao processamento das imagens são alcançados níveis de acurácia em torno de 90%, com uso do algoritmo baseado em 1-NN. Além disso, a técnica 1-NN apresentou maior tolerância aos problemas de luminosidade, em contrapartida, demandam maior poder de processamento quando comparados com o algoritmo baseado em watershed. Os resultados mostram que o sistema proposto é uma solução eficiente e de custo relativamente baixo em comparação com os meios tradicionais de monitoramento e pode ser acoplada em um VANT para realizar o processamento durante o voo