ANÁLISE TEMPORAL DA COBERTURA FLORESTAL E USO DA TERRA NA APA DE GUARATUBA – PR

O crescimento e ocupação desordenada das terras contribuem para a perda de diversidade biológica nos biomas brasileiros. A criação de áreas especialmente protegidas, como as Áreas de Proteção Ambiental, unidades de conservação da categoria uso sustentável, consiste em um instrumento eficiente para frear tal cenário. Nesse sentido, a APA de Guaratuba, no estado do Paraná, desempenha importante papel de proteção da biodiversidade e controle da ocupação deste espaço, no entanto, é essencial que existam mecanismos de avaliação periódica do seu estado de conservação, por meio da identificação de ameaças que possam surgir à área protegida. Deste modo, o presente estudo objetivou quantificar a dinâmica da ocupação da terra na Área de Proteção Ambiental de Guaratuba – PR. Mapas temáticos de classificação da cobertura da terra foram elaborados para os anos de 1992 e 2017, no intuito de analisar as alterações ocorridas na área de estudo neste mesmo período. Foram utilizadas imagens a partir do TM Landsat, realizou-se a correção atmosférica das imagens com base no método Dark Object Subtraction (DOS). O sistema de projeção adotado foi o Universal Transversa de Mercator – UTM, fuso 22 Sul, Datum WGS-84. Foram definidas as classes de uso e iniciou-se o processo de classificação das imagens, empregando o Software ArcGis para a classificação supervisionada, a partir do algoritmo Maximum Likelihood – Maxver. Os resultados mostram que, ao longo dos 25 anos as áreas ocupadas por floresta nativa, agricultura e áreas consolidadas aumentaram, ao passo que as áreas ocupadas com água, campos, mangue e reflorestamentos diminuíram. Os coeficientes Kappa determinados para as classificações tanto de 1992 quanto 2017 apresentaram qualidade “muito boa” ou “concordância substancial”, confirmando a acurácia da amostragem. Conclui-se que as principais modificações ocorreram nas classes agricultura e reflorestamento, havendo o aumento das áreas utilizadas para agricultura (0,60%) e diminuição dos reflorestamentos (-0,61%).

Neural Network Classifier of Oil Pollution on the Water Surface when Processing Radar Images

The paper proposes a solution to the problem of detecting oil pollution on a monochrome radar image. The detection of oil pollution in the image includes the solution of three tasks: detecting a dark object on the image, highlighting the main characteristics of a dark object, classifying a dark object as oil pollution or natural slick. Various characteristics of a dark object are proposed based on the contrast between the object and the background. It is proposed to use a neural network as a classifier. The input parameters of the neural network classifier of the dark image object are proposed. A technique for determining the structure of a neural classifier is presented. An algorithm for testing the selected structure of the neural network for the suitability of classifying the dark area on the image of the water surface as oil pollution or wind slick is proposed. The results of the work of the neural network classifier program for detecting abnormal objects in radar images are demonstrated.

Images from the surface of asteroid Ryugu show rocks similar to carbonaceous chondrite meteorites

The near-Earth asteroid (162173) Ryugu is a 900-m-diameter dark object expected to contain primordial material from the solar nebula. The Mobile Asteroid Surface Scout (MASCOT) landed on Ryugu’s surface on 3 October 2018. We present images from the MASCOT camera (MASCam) taken during the descent and while on the surface. The surface is covered by decimeter- to meter-sized rocks, with no deposits of fine-grained material. Rocks appear either bright, with smooth faces and sharp edges, or dark, with a cauliflower-like, crumbly surface. Close-up images of a rock of the latter type reveal a dark matrix with small, bright, spectrally different inclusions, implying that it did not experience extensive aqueous alteration. The inclusions appear similar to those in carbonaceous chondrite meteorites.

Anchor Box Parameters and Bounding Box Overlap Ratios for the Faster R-CNN Detector in Detecting a Single Object by the Masking Background

Anchor box parameters and bounding box overlap ratios are studied in order to set them appropriately for the Faster R-CNN detector. The benchmark detection is based on monochrome images whose background may mask a small dark object. Three object detection tasks are generated, where every image either contains a small black square/rectangle or does not contain the object, representing thus class “background”. The ratios are recommended to be tried at 0.7 if this class is represented. The ratio for positive training samples is tried at a less value but greater than 0.4 for the task every image of which contains an object. The minimum anchor box size is better to try at a lesser value from a range of object sizes. The anchor box pyramid scale factor and the number of levels are better to try at 2 and 8, respectively. Subsequently, these parameters may be corrected as their influence is fuzzier than that of the ratios.

Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV

Imaging from low altitudes is nowadays commonly used in remote sensing and photogrammetry. More and more often, in addition to acquiring images in the visible range, images in other spectral ranges, e.g., near infrared (NIR), are also recorded. During low-altitude photogrammetric studies, small-format images of large coverage along and across the flight route are acquired that provide information about the imaged objects. The novelty presented in this research is the use of the modified method of the dark-object subtraction technique correction with a modified Walthall’s model for correction of images obtained from a low altitude. The basic versions of these models have often been used to radiometric correction of satellite imagery and classic aerial images. However, with the increasing popularity of imaging from low altitude (in particular in the NIR range), it has also become necessary to perform radiometric correction for this type of images. The radiometric correction of images acquired from low altitudes is important from the point of view of eliminating disturbances which might reduce the capabilities of image interpretation. The radiometric correction of images acquired from low altitudes should take into account the influence of the atmosphere but also the geometry of illumination, which is described by the bidirectional reflectance distribution function (BRDF). This paper presents a method of radiometric correction for unmanned aerial vehicle (UAV) NIR images. The study presents a method of low-altitude image acquisition and a fusion of the method of the dark-object subtraction technique correction with a modified Walthall’s model. The proposed solution performs the radiometric correction of images acquired in the NIR range with the root mean square error (RMSE) value not exceeding 10% with respect to the original images. The obtained results confirm that the proposed method will provide effective compensation of radiometric disturbances in UAV images.