ANALIZA RGB I MULTISPEKTRALNE KAMERE NA BESPILOTNOME ZRAKOPLOVU ZA KLASIFIKACIJU KUKURUZA STROJNIM UČENJEM

This study investigated a crop and soil classification applying the Random Forest machine learning algorithm based on the red-green-blue (RGB) and multispectral sensor imaging deploying an unmanned aerial vehicle (UAV). The study area covered two 10 x 10 m subsets of a maize-sown agricultural parcel near Koška. The highest overall accuracy was obtained in the combination of the red edge (RE), near-infrared (NIR), and normalized difference vegetation index (NDVI) in both subsets, with a 99.8% and 91.8% overall accuracy, respectively. The conducted analysis proved that the RGB camera obtained sufficient accuracy and was an acceptable solution to the soil and vegetation classification. Additionally, a multispectral camera and spectral analysis allowed for a more detailed analysis, primarily of the spectrally similar areas. Thus, this procedure represents a basis for both the crop density calculation and weed detection while deploying an unmanned aerial vehicle. To ensure crop classification effectiveness in practical application, it is necessary to further integrate the weed classes in the current vegetation class and separate them into crop and weed classes.U ovoj studiji istražena je klasifikacija usjeva i tla korištenjem algoritma strojnoga učenja Random Forest, temeljenoga na crveno-zeleno-plavoj (RGB) i multispektralnoj kameri integriranoj na bespilotnome zrakoplovu. Područje istraživanja obuhvaćalo je dva podskupa poljoprivredne čestice kukuruza dimenzija 10 x 10 m u blizini Koške. Najveća ukupna točnost klasifikacije postignuta je u kombinaciji rubnoga crvenog (RE), bliskoga infracrvenog (NIR) kanala i indeksa normalizirane vegetacijske razlike (NDVI) u oba podskupa, s ukupnom točnošću od 99,8 %, odnosno 91,8 %. Provedena analiza pokazala je da je RGB kamera postigla dovoljnu točnost i da je prihvatljivo rješenje za klasifikaciju tla i vegetacije. Međutim, multispektralna kamera i spektralna analiza omogućile su detaljniju analizu, prvenstveno za spektralno slična područja. Ovaj je postupak temelj i za izračun gustoće usjeva i za otkrivanje korova s pomoću bespilotnih zrakoplova. Kako bi se osigurala učinkovitost klasifikacije usjeva u praktičnoj primjeni, potrebno je dodatno uključiti klase korova u trenutačnu klasu vegetacije i podijeliti ih na klase usjeva i korova

Aplikasi Citra Drone untuk Klasifikasi Vegetasi di Cagar Alam Curah Manis Sempolan 1 Menggunakan Metode Manual, Object Base Image Analysis (OBIA), dan K-Means

Nowadays, vegetation classification can be used to find out the latest information about the characteristics and distribution of vegetation in an area. However, a conservative process to differentiate vegetation was ineffective. Some of those limitations are poor accessibility that does work less safety, time-consuming, and needs a lot of human resources. On the other hand, remote sensing offers solutions that cannot be done by the simple method, such as how to take the data, time-consuming are less, and human resource needs are less as well. The purpose of this study was to classify, measured the area of each vegetation, and compared the effectiveness of the unsupervised used K-Means algorithm and supervised used Object Base Image Analysis algorithm methods vegetation classification. For accuracy calculation with confusion matrix, the classification results of the two methods were compared with the manual digitization method. Data was taken using drones in the area of the Curah Manis Sempolan Nature Reserve 1. Classification of vegetation consists of 5 vegetation types, which was apak, bush, pine, bendo, and dadap. The total area of the study area was 1.633 ha, and area vegetation of each classification was apak 0.224 ha; bush 0.748 ha; pine 0.394 ha; bendo 0.222 ha; and dadap 0.045 ha. The results of the calculation of accuracy showed that the unsupervised method had a value for overall accuracy of 80% and kappa accuracy of 73.58%. Then, in the supervised for overall accuracy is 68% and kappa accuracy of 58.72%. Keywords: classification, drone, remote sensing, satellit

Comparations of Supervised Machine Learning Techniques in Predicting the Classification of the Household’s Welfare Status

Poverty has been a major problem for most countries around the world, including Indonesia. One approach to eradicate poverty is through equitable distribution of social assistance for target households based on Integrated Database of social assistance. This study has compared several well-known supervised machine learning techniques, namely: Naïve Bayes Classifier, Support Vector Machines, K-Nearest Neighbor Classification, C4.5 Algorithm, and Random Forest Algorithm to predict household welfare status classification by using an Integrated Database as a study case. The main objective of this study was to choose the best-supervised machine learning approach in predicting the classification of household’s welfare status based on attributes in the Integrated Database. The results showed that the Random Forest Algorithm was the best

CROP CLASSIFICATION ON SINGLE DATE SENTINEL-2 IMAGERY USING RANDOM FOREST AND SUPPOR VECTOR MACHINE

Mapping of the crop using satellite images is a challenging task due to complexities within field, and having the similar spectral properties with other crops in the region. Recently launched Sentinel-2 satellite has thirteen spectral bands, fast revisit time and resolution at three different level (10 m, 20 m, 60 m), as well as the free availability of data, makes it a good choice for vegetation mapping. This study aims to classify crop using single date Sentinel-2 imagery in the Roorkee, district Haridwar, Uttarakhand, India. Classification is performed by using two most popular and efficient machine learning algorithms: Random Forest (RF) and Support Vector Machine (SVM). In this study, four spectral bands, i.e., Near Infrared, Red, Green, and Blue of Sentinel-2 satellite are stacked for the classification. Results show that overall accuracy of the classification achieved by RF and SVM using Sentinel-2 imagery are 84.22% and 81.85% respectively. This study demonstrates that both classifiers performed well by setting an optimal value of tuning parameters, but RF achieved 2.37% higher overall accuracy over SVM. Analysis of the results states that the class specific accuracies of High-Density Forest attain the highest accuracy whereas Fodder class reports the lowest accuracy. Fodder achieve lowest accuracy because there is an intermixing of pixels among Wheat and Fodder crops. In this study, it is found that RF shows better potential in classifying crops more accurately in comparison to SVM and Sentinel-2 has great potential in vegetation mapping domain in remote sensing

Inconsistencies of the permanent preservation areas of the rural environmental registry through Geobia

As regras de exploração e de conservação da vegetação nativa, no Brasil, estão instituídas no Novo Código Florestal. O registro das informações dos imóveis rurais é realizado a partir do Cadastro Ambiental Rural (CAR). O objetivo desse estudo édesenvolver e avaliar uma metodologia para mapear conflitos da cobertura e uso da terra em APPs averbadas no CAR, integrando imagens orbitais de multissensores e de multirresoluções com GEOBIA. Para isso, foram utilizadas imagens Sentinel 1 e 2A. As bandas ópticas do Sentinel 2A com resolução espacial de 10 m foram utilizadas no processo de segmentação, considerando os parâmetros limiar de similaridade e o Tamanho Mínimo do Objeto (TMO). A segmentação com a menor distância Euclidiana (D) foi utilizada na avaliação dos parâmetros do método Random Forest(RF). Os resultados mostram que o desvio padrão das texturas Momento da Diferença Inversa e Variância foram os mais relevantes para discriminar as classes de cobertura e uso da terra. Nas APPs averbadas em áreas menores que 1 módulo fiscal e entre 1 e 2 módulos fiscais, a maior parte foi classificada como Campo e Mata nativa. Nas APPs averbadas em propriedades entre 2 e 4 módulos fiscais e maiores que 4 módulos fiscais, observa-se aumento de Solo exposto e Agricultura. Os resultados permitiram quantificar que as maiores inconsistências entre as APPs averbadas e a classificação da cobertura e uso da terra foram identificadas em propriedades superiores a 4 módulos.In Brazil, the rules for exploration and conservation of native vegetation are established in the New Forest Code, the registration of information on rural properties is carried out through the Rural Environmental Registry (CAR). The objective of the study is to develop and evaluate a methodology for mapping the inconsistencies between land cover and land use with PermanentPreservation Areas (APPs) recorded in the CAR. For this, Sentinel 1 and 2A images were used. The optical bands of Sentinel 2A with spatial resolution of 10 m were used in the segmentation process, considering the similarity threshold parameters and Minimum Object Size (TMO). Segmentation with the shortest Euclidean distance (D) was used in the evaluation of the parameters of the Random Forest (RF) method. The standard deviation of the Moment of the Inverse Difference and Variance textures were the most relevant to discriminate the classes of land cover and use. For APPs registered with areas smaller than 1 fiscal module, for a total of 95.96 ha, most were classified as native Field and Forest. In areas between 1 and 2 fiscal modules, total of 119.62 ha, the most prevalent classes were also the native Campo and Mata classes

INCONSISTÊNCIAS DAS ÁREAS DE PRESERVAÇÃO PERMANENTE DO CADASTRO AMBIENTAL RURAL POR MEIO DA GEOBIA: Inconsistencies of the Permanent Preservation Areas of the Rural Environmental Registry through GEOBIA

In Brazil, the rules for exploration and conservation of native vegetation are established in the New Forest Code, the registration of information on rural properties is carried out through the Rural Environmental Registry (CAR). The objective of the study is to develop and evaluate a methodology for mapping the inconsistencies between land cover and land use with Permanent Preservation Areas (APPs) recorded in the CAR. For this, Sentinel 1 and 2A images were used. The optical bands of Sentinel 2A with spatial resolution of 10 m were used in the segmentation process, considering the similarity threshold parameters and Minimum Object Size (TMO). Segmentation with the shortest Euclidean distance (D) was used in the evaluation of the parameters of the Random Forest (RF) method. The standard deviation of the Moment of the Inverse Difference and Variance textures were the most relevant to discriminate the classes of land cover and use. For APPs registered with areas smaller than 1 fiscal module, for a total of 95.96 ha, most were classified as native Field and Forest. In areas between 1 and 2 fiscal modules, total of 119.62 ha, the most prevalent classes were also the native Campo and Mata classes.No Brasil, as regras de exploração e de conservação da vegetação nativa estão instituídas no Novo Código Florestal, o registro das informações dos imóveis rurais é realizado através do Cadastro Ambiental Rural (CAR). O objetivo do estudo é desenvolver e avaliar uma metodologia para o mapeamento das inconsistências entre a cobertura e o uso da terra com Áreas de Preservação Permanente (APPs) averbadas no CAR. Para isso, foram utilizadas imagens Sentinel 1 e 2A. As bandas ópticas do Sentinel 2A com resolução espacial de 10 metros foram utilizadas no processo de segmentação, considerando os parâmetros limiar de similaridade e o Tamanho Mínimo do Objeto (TMO). A segmentação com a menor distância Euclidiana (D) foi utilizada na avaliação dos parâmetros do método Random Forest (RF). O desvio padrão das texturas Momento da Diferença Inversa e Variância foram os mais relevantes para discriminar as classes de cobertura e uso da terra. Para APPs averbadas com áreas menores que 1 módulo fiscal, num total de 95,96 hectares, a maior parte foi classificada como Campo e Mata nativa. Em áreas entre 1 e 2 módulos fiscais, total de 119,62 hectares, as classes mais predominantes também foram as classes Campo e Mata nativa

Evaluation of segmentation parameters in OBIA for classification of land covers from UAV images

[EN] Unmanned Aerial Vehicles (UAVs) have given a new boost to remote sensing and image classification techniques due to the high level of detail among other factors. Object-based image analysis (OBIA) could improve classification accuracy unlike to pixel-based, especially in high-resolution images. OBIA application for image classification consists of three stages i.e., segmentation, class definition and training polygons, and classification. However, defining the parameters: spatial radius (SR), range radius (RR) and minimum region size (MR) is necessary during the segmentation stage. Despite their relevance, they are usually visually adjusted, which leads to a subjective interpretation. Therefore, it is of utmost importance to generate knowledge focused on evaluating combinations of these parameters. This study describes the use of the mean-shift segmentation algorithm followed by Random Forest classifier using Orfeo Toolbox software. It was considered a multispectral orthomosaic derived from UAV to generate a suburban map land cover in town of El Pueblito, Durango, Mexico. The main aim was to evaluate efficiency and segmentation quality of nine parameter combinations previously reported in scientific studies.This in terms of number generated polygons, processing time, discrepancy measures for segmentation and classification accuracy metrics. Results evidenced the importance of calibrating the input parameters in the segmentation algorithms. Best combination was RE=5, RR=7 and TMR=250, with a Kappa index of 0.90 and shortest processing time. On the other hand, RR showed a strong and inversely proportional degree of association regarding the classification accuracy metrics.[ES] Los Vehículos Aéreos No Tripulados (VANT) han otorgado un nuevo auge a la teledetección y a las técnicas d clasificación de imágenes debido al alto nivel de detalle entre otros factores. El análisis de imágenes basado en objetos (OBIA) puede mejorar la precisión en la clasificación a diferencia de la basada en píxeles, especialmente en imágenes de alta resolución. La aplicación de OBIA para la clasificación de imágenes consta de tres etapas i.e., segmentación, definición de clases y polígonos de entrenamiento y clasificación. No obstante, en la etapa de segmentación es necesario definir los parámetros: radio espacial (RE), radio de rango (RR) y tamaño mínimo de la región (TMR). Los cuales, pese a su relevancia, suelen ser ajustados de manera visual, lo que conlleva a una interpretación subjetiva. Por lo anterior, es de suma importancia generar conocimiento enfocado a evaluar las combinaciones de estos parámetros. Este estudio describe el uso del algoritmo de segmentación de desplazamiento medio, seguido del clasificador Random Forest mediante el software Orfeo Toolbox. Se consideró un ortomosaico multiespectral derivado de VANT para generar un mapa de cobertura de suelo sub-urbano en la localidad El Pueblito, Durango, México. El objetivo principal fue evaluar la eficiencia y calidad de segmentación de nueve combinaciones de parámetros anteriormente reportadas en estudios científicos. Ello en términos de número de polígonos generados, tiempo de procesamiento, medidas de discrepancia de segmentación y métricas de precisión de la clasificación. Los resultados obtenidos lograron evidenciar la importancia de ajustar los parámetros de entrada en los algoritmos de segmentación. La mejor combinación fue RE=5, RR=7 y TMR=250, con un índice de Kappa de 0,90 y el menor tiempo de procesamiento. 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Uncertainty analysis of object-based land cover classification using time series of Sentinel-2 data

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification

A framework for scale-sensitive, spatially explicit accuracy assessment of binary built-up surface layers

To better understand the dynamics of human settlements, thorough knowledge of the uncertainty in geospatial built-up surface datasets is critical. While frameworks for localized accuracy assessments of categorical gridded data have been proposed to account for the spatial non-stationarity of classification accuracy, such approaches have not been applied to (binary) built-up land data. Such data differs from other data such as land cover data, due to considerable variations of built-up surface density across the rural-urban continuum resulting in switches of class imbalance, causing sparsely populated confusion matrices based on small underlying sample sizes. In this paper, we aim to fill this gap by testing common agreement measures for their suitability and plausibility to measure the localized accuracy of built-up surface data. We examine the sensitivity of localized accuracy to the assessment support, as well as to the unit of analysis, and analyze the relationships between local accuracy and density / structure-related properties of built-up areas, across rural-urban trajectories and over time. Our experiments are based on the multi-temporal Global Human Settlement Layer (GHSL) and a reference database for the state of Massachusetts (USA). We find strong variation of suitability among commonly used agreement measures, and varying levels of sensitivity to the assessment support. We then apply our framework to assess localized GHSL data accuracy over time from 1975 to 2014. Besides increasing accuracy along the rural-urban gradient, we find that accuracy generally increases over time, mainly driven by peri-urban densification processes in our study area. Moreover, we find that localized densification measures derived from the GHSL tend to overestimate peri-urban densification processes that occurred between 1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.Comment: 28 pages, 17 figure