This study proposes and benchmarks a lightweight pipeline for large-scale parcel-level crop classification using time-series data. The key contributions include a scalable SITS download strategy, a novel parcel-level crop classification dataset for the USA States of Texas and California, and a benchmark of classification techniques tested under varied data availability scenarios, highlighting the pipeline’s potential for enhancing agricultural monitoring systems. The methodology extracts basic descriptive statistics integrating with agricultural and cloud filtering, leveraging field boundaries delineation and USDA Cropland Data Layer datasets. Experimental results show that pretrained and fine-tuned models, such as SITS-BERT, outperform Random Forest and Support Vector Machine approaches, achieving an F1 score of 98.2% and overall accuracy of 99.3% on the Texas dataset for the more abundant training data scenarios. The pipeline has a projected computational speed-up of at leastat least 2, 500× compared to pixel-based methods for the tested datasets. The download pipeline is available on AgriGEE.lite library in https://pypi.org/project/agrigee-lite. The full benchmark results and related code are available at https://github.com/mateuspinto/light-crop-classification.

Binary segmentation is the main task underpinning several remote sensing applications, which are particularly interested in identifying and monitoring a specific category/object. Although extremely important, such a task has several challenges, including huge intra-class variance for the background and data imbalance. Furthermore, most works tackling this task partially or completely ignore one or both of these challenges and their developments. In this paper, we propose a novel method to perform imbalanced binary segmentation of remote sensing images based on deep networks, prototypes, and contrastive loss. The proposed approach allows the model to focus on learning the foreground class while alleviating the class imbalance problem by allowing it to concentrate on the most difficult background examples. The results demonstrate that the proposed method outperforms state-of-the-art techniques for imbalanced binary segmentation of remote sensing images while taking much less training time.

When it comes to technology in agriculture, one of the most important aspects is farmland crop monitoring. However, in most cases, only the main crops are needed to be monitored by satellite images, due to their high territorial extension. Therefore, a semantic segmentation model for identifying plantations should correctly classify the majority classes and also automatically identify other unknown crops. Open set recognition (OSR) aims to embrace both of these causes, so that the model can be more robust in the wild. This work adapts the framework of outlier exposure (OE) for open set image segmentation. OE was evaluated by adding it to three distinct methods for open set segmentation: softmax thresholding, OpenPCS and OpenPCS++. We conducted several experiments to enrich the discussion of the impact of OE on the semantic segmentation of crop imagery. Our methodology achieved a consistent increase for OpenPCS and OpenPCS++ methods, with an improvement of up to 7.5% in terms of area under the receiver operating characteristic (AUROC) curve if compared to previous work.

In traditional semantic segmentation, knowing about all existing classes is essential to yield effective results with the majority of existing approaches. However, these methods trained in a Closed Set of classes fail when new classes are found in the test phase, not being able to recognize that an unseen class has been fed. This means that they are not suitable for Open Set scenarios, which are very common in real-world computer vision and remote sensing applications. In this paper, we discuss the limitations of Closed Set segmentation and propose two fully convolutional approaches to effectively address Open Set semantic segmentation: OpenFCN and OpenPCS. OpenFCN is based on the well-known OpenMax algorithm, configuring a new application of this approach in segmentation settings. OpenPCS is a fully novel approach based on feature-space from DNN activations that serve as features for computing PCA and multi-variate gaussian likelihood in a lower dimensional space. In addition to OpenPCS and aiming to reduce the RAM memory requirements of the methodology, we also propose a slight variation of the method (OpenIPCS) that uses an iteractive version of PCA able to be trained in small batches. Experiments were conducted on the well-known ISPRS Vaihingen/Potsdam and the 2018 IEEE GRSS Data Fusion Challenge datasets. OpenFCN showed little-to-no improvement when compared to the simpler and much more time efficient SoftMax thresholding, while being some orders of magnitude slower. OpenPCS achieved promising results in almost all experiments by overcoming both OpenFCN and SoftMax thresholding. OpenPCS is also a reasonable compromise between the runtime performances of the extremely fast SoftMax thresholding and the extremely slow OpenFCN, being able to run close to real-time. Experiments also indicate that OpenPCS is effective, robust and suitable for Open Set segmentation, being able to improve the recognition of unknown class pixels without reducing the accuracy on the known class pixels. We also tested the scenario of hiding multiple known classes to simulate multimodal unknowns, resulting in an even larger gap between OpenPCS/OpenIPCS and both SoftMax thresholding and OpenFCN, implying that gaussian modeling is more robust to settings with greater openness.

We propose a novel deep metric learning method. Differently from many works in this area, we define a novel latent space obtained through an autoencoder. The new space, namely S-space, is divided into different regions describing positions where pairs of objects are similar/dissimilar. We locate makers to identify these regions and estimate the similarities between objects through a kernel-based Cauchy distribution to measure the markers’ distance and the new data representation. In our approach, we simultaneously estimate the markers’ position in the S-space and represent the objects in the same space. Moreover, we propose a new regularization function to prevent similar markers from collapsing altogether. Our method emphasizes the group property (separability) while preserving instance representativity. We present evidence that our proposal can represent complex spaces, for instance, when groups of similar objects are located in disjoint regions. We compare our proposal to nine different distance metric learning approaches (four of them are based on deep learning) on 28 real-world heterogeneous datasets. According to the four quantitative metrics used, our method overcomes all of the nine strategies from the literature.

Semantic segmentation is a classic computer vision task with multiple applications, which includes medical and remote sensing image analysis. Despite recent advances with deep-based approaches, labeling samples (pixels) for training models is laborious and, in some cases, unfeasible. In this paper, we present two novel meta-learning methods, named WeaSeL and ProtoSeg, for the few-shot semantic segmentation task with sparse annotations. We conducted an extensive evaluation of the proposed methods in different applications (12 datasets) in medical imaging and agricultural remote sensing, which are very distinct fields of knowledge and usually subject to data scarcity. The results demonstrated the potential of our method, achieving suitable results for segmenting both coffee/orange crops and anatomical parts of the human body in comparison with full dense annotation.

Open set segmentation is a relatively new and unexplored task, with just a handful of methods proposed to model such tasks. We propose a novel method called CoReSeg that tackles the issue using class conditioned reconstruction of the input images according to their pixelwise mask. Our method conditions each input pixel to all known classes, expecting higher errors for pixels of unknown classes. It was observed that the proposed method produces better semantic consistency in its predictions than the baselines, resulting in cleaner segmentation maps that better fit object boundaries. CoReSeg outperforms state-of-the-art methods on the Vaihingen and Potsdam ISPRS datasets, while also being competitive on the Houston 2018 IEEE GRSS Data Fusion dataset. Our official implementation for CoReSeg is available at: https://github.com/iannunes/CoReSeg.

Multitemporal remote-sensing images play a key role as a source of information for automated crop mapping and monitoring. The spatial/spectral pattern evolution along time provides information about the dynamics of the crops and are very useful for productivity estimation. Although the multitemporal mapping of crops has progressed considerably with the advent of deep learning in recent years, the classification models obtained still have limitations when exposed to unknown classes in the prediction phase, reducing their usefulness. In other words, these models are trained to identify a closed set of crops (e.g., soy and sugar cane) and are therefore unable to recognize other types of crops (e.g., maize). In this letter, we deal with the challenges of multitemporal crop recognition by proposing a new approach called OpenPCS++ that is not only able to learn known classes but is also capable of identifying new crops in the predicting phase. The proposed approach was evaluated in two challenging public datasets located in tropical climates in Brazil. Results showed that OpenPCS++ achieved increases of up to 0.19 in terms of area under the receiver-operating characteristic (ROC) curve in comparison with baselines. Code is available athttps://github.com/DiMorten/osss-mcr.

Current deep learning methods for interpreting seismic images require large amounts of labeled data, and due to strategic and economic interests, these data are not plenty available. In this scenario, seismic interpretation can benefit from self-supervised learning by relying on prior training without manually-annotated labels within the target data domain and subsequent fine-tuning with few-shot. To demonstrate the potential of such an approach, we conducted experiments with three classic context-based pretext tasks: rotation, jigsaw, and frame order prediction. Our results for 1, 5, 10 and 20-shots showed significant improvement for mean Intersection-over-Union (mIoU) measurements for semantic segmentation in most scenarios, outperforming the baseline method in 38% in the 1-shot scenario for the F3 Netherlands Dataset, and 16.4% in the New Zealand Parihaka dataset, and this gap grows even higher after performing ensemble modeling. These experiments suggest that applying SSL methods can also bring great benefits in seismic interpretation when few labeled data are available.

Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spaces, making failures in recognition a hazard to deep visual learning. Open set recognition methods are characterized by the ability to correctly identifying inputs of known and unknown classes. In this context, we propose GeMOS: simple and plug-and-play open set recognition modules that can be attached to pretrained Deep Neural Networks for visual recognition. The GeMOS framework pairs pre-trained Convolutional Neural Networks with generative models for open set recognition to extract open set scores for each sample, allowing for failure recognition in object recognition tasks. We conduct a thorough evaluation of the proposed method in comparison with state-of-the-art open set algorithms, finding that GeMOS either outperforms or is statistically indistinguishable from more complex and costly models.

Object detection and image segmentation are essential for environmental monitoring. This task can be performed and automated using machines with the processing capabilities of convolutional neural networks (CNN), achieving outstanding performance thanks to the current computing capacity and data available. Still, there are advances to perceive and questions on how to get the optimal performance and improve the results. With this aim, we assessed a state-of-the-art CNN, the Dynamic Dilated Convolution Neural Network (DDCN), to segment trees inside an urban environment. We chose DDCN because it exploits the paradigm of multi-context without increasing the number of trainable parameters of the network and defines, while training, the best patch size that should be used by the network in the test phase, helping to tune the CNN. With this technique, we achieved: pixel accuracy of 95.86%; average accuracy of 90.63%; F1-score of 90.93%; Kappa index of 81.87% and IoU of 72.78%. We are showing the capabilities of this CNN to segment complex images.

Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community. Many works have focused on encoding skeleton data as skeleton image representations based on spatial structure of the skeleton joints, in which the temporal dynamics of the sequence is encoded as variations in columns and the spatial structure of each frame is represented as rows of a matrix. To further improve such representations, we introduce a novel skeleton image representation to be used as input of Convolutional Neural Networks (CNNs), named SkeleMotion. The proposed approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Different temporal scales are employed to compute motion values to aggregate more temporal dynamics to the representation making it able to capture long-range joint interactions involved in actions as well as filtering noisy motion values. Experimental results demonstrate the effectiveness of the proposed representation on 3D action recognition outperforming the state-of-the-art on NTU RGB+D 120 dataset.

In several applications, the use of pixel contextual information has led to significant effectiveness results in remote sensing image classification tasks. In this paper, we investigate the use of deep superpixel context representation for detection in aerial images. Experimental results considering the widely used ISPRS Postdam and Munich Vehicle datasets demonstrate that max pooling and standard deviation are the most promising pooling methods, while the concatenation of contextual features from different pooling approaches leads to an effective characterization of superpixel contextual information in remote sensing images.

Suitable time series representations play an important role in classification tasks. In this letter, we investigate the use of recurrence-plot-(RP)-based representations in the classification of eucalyptus regions in remote sensing images. The proposed framework is composed of three steps. First, time series associated with image pixels are represented by RP images; next, RP images are characterized by means of visual description approaches; finally, we use a soft computing framework based on genetic programing to discover an effective combination of time series dissimilarity functions to combine extracted features. Performed experiments in a eucalyptus classification problem demonstrated that the proposed framework is effective when compared to approaches based on the use of time series itself.

Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating, and identifying plant species through time and space. However, this is a challenging task given the high volume of data, the constant data missing from temporal dataset, the heterogeneity of temporal profiles, the variety of plant visual patterns, and the unclear definition of individuals' boundaries in plant communities. In this letter, we propose a novel method, suitable for phenological monitoring, based on convolutional networks (ConvNets) to perform spatio-temporal vegetation pixel classification on high-resolution images. We conducted a systematic evaluation using high-resolution vegetation image datasets associated with the Brazilian Cerrado biome. Experimental results show that the proposed approach is effective, overcoming other spatio-temporal pixel-classification strategies.

Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Toward such goal, convolutional networks can learn specific and adaptable features based on the data. However, these networks are not capable of processing a whole remote sensing image, given its huge size. To overcome such limitation, the image is processed using fixed size patches. The definition of the input patch size is usually performed empirically (evaluating several sizes) or imposed (by network constraint). Both strategies suffer from drawbacks and could not lead to the best patch size. To alleviate this problem, several works exploited multicontext information by combining networks or layers. This process increases the number of parameters, resulting in a more difficult model to train. In this paper, we propose a novel technique to perform semantic segmentation of remote sensing images that exploits a multicontext paradigm without increasing the number of parameters while defining, in training time, the best patch size. The main idea is to train a dilated network with distinct patch sizes, allowing it to capture multicontext characteristics from heterogeneous contexts. While processing these varying patches, the network provides a score for each patch size, helping in the definition of the best size for the current scenario. A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing data sets with very distinct properties. Our results show that the proposed algorithm provides improvements in pixelwise classification accuracy when compared to the state-of-the-art methods.

In this paper, we introduce a novel representation to encode contextual information in object-based remote sensing image classification problems. The solution relies on the creation of a visual codebook and its use to compute the co-occurrence of visual words within a superpixel and within its neighboring regions. Performed experiments on the well-known collections (grss_dfc_2014 and ISPRS Potsdam) demonstrate that the proposed approach is effective, yielding comparable or better results than several baselines.

Mapping rural buildings in developing countries is crucial to monitor and plan in those vulnerable areas. Despite the existence of some rural building annotations in OpenStreetMap (OSM), those are of insufficient quantity and quality to train models able to map large areas accurately. In particular, these annotations are very often misaligned with respect to the buildings that are present in updated aerial imagery. We propose a Markov Random Field (MRF) method to correct misaligned rural building annotations. To do so, our method uses i) the correlation between candidate aligned OSM annotations and buildings roughly detected on aerial images and ii) the local consistency of the alignment vectors.

Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure toward flood monitoring is based on identifying the area most vulnerable to flooding, which gives authorities relevant regions to focus. In this letter, we propose several methods to perform flooding identification in high-resolution remote sensing images using deep learning. Specifically, some proposed techniques are based upon unique networks, such as dilated and deconvolutional ones, whereas others were conceived to exploit diversity of distinct networks in order to extract the maximum performance of each classifier. The evaluation of the proposed methods was conducted in a high-resolution remote sensing data set. Results show that the proposed algorithms outperformed the state-of-the-art baselines, providing improvements ranging from 1% to 4% in terms of the Jaccard Index.

This paper introduces a two-step hyper- and multi-spectral image classification approach. The first step relies on the use of a genetic programming (GP) framework to both select and combine appropriate bands. The second step is concerned with the image classification itself. We present two strategies for multi-class classification problems based on the combination of GP-based indices defined in binary classification scenarios. Performed experiments involving well-known and widely-used datasets demonstrate that the proposed approach yields comparable or better effectiveness performance when compared to several traditional baselines.

Vegetation segmentation in high resolution images acquired by unmanned aerial vehicles (UAVs) is a challenging task that requires methods capable of learning high-level features while dealing with fine-grained data. In this paper, we propose a combination of different methods of semantic segmentation based on Convolutional Networks (ConvNets) to obtain highly accurate segmentation of individuals of different vegetation species. The objective is not only to learn specific and adaptable features depending on the data, but also to learn and combine appropriate classifiers. We conducted a systematic evaluation using a high-resolution UAV-based image dataset related to a campo rupestre vegetation in the Brazilian Cerrado biome. Experimental results show that the ensemble technique overcomes all segmentation strategies.

This paper proposes a new approach for contextual feature extraction from superpixels in aerial urban scenes. Our method extracts features with many levels of context from superpixels by exploiting different layers of a pre-trained convolutional neural network. Experimental results show the effectiveness of the proposed approach, which outperforms traditional methods based on handcrafted feature extraction algorithms.

In this work, we propose two main contributions to hyperspectral image interpretation. Firstly, while the traditional Weighted Linear Combination optimized by Genetic Algorithms (WLC-GA) [1] intends to give more discriminant power to those classification approaches contributing the most, we extend it to make a fine tuning over the class probabilities within the combination process. Then, we compare both methods (WLC-GA and its extension) with a more complex non-linear meta learning strategy called Stacked Generalization in which Support Vector Machines with Radial Basis Function kernel was used as combiner [2]. The experimental results, considering two widely used data sets, the Indian Pines and the Pavia University, are conducted in three different scenarios. Results show that both WLC-GA and its extended version achieve the best overall accuracy, and the proposed classification approach overcomes the accuracies of the other traditional ones used in this study.

In this paper, we propose a post classification smoothing method aimed at improving the accuracy and visual appearance of sub-decimeter image classification results. Starting from the class confidence maps of a supervised classifier, we find a set of high confidence markers and propagate labels on an extended region adjacency graph. We apply the proposed method on a challenging 5cm resolution dataset over Potsdam, Germany. The proposed algorithm outperforms state-of-the-art post classification smoothing algorithms both when the classifier is trained specifically on the image and when it is trained and tested in different set of images.

Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, as commonly proposed methods for this task are based on custom-tailored features, they are limited by modeling assumptions about printing artifacts. In this paper, we explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. This allows us to avoid any prior assumption about printing artifacts that characterize each printer, thus highlighting almost invisible and difficult printer footprints generated during the printing process. The proposed approach merges, in a synergistic fashion, convolutional neural networks (CNNs) applied on multiple representations of multiple data. Multiple representations, generated through different pre-processing operations, enable the use of the small and lightweight CNNs whilst the use of multiple data enable the use of aggregation procedures to better determine the provenance of a document. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem.

Suitable feature representation is essential for performing video analysis and understanding in applications within the smart surveillance domain. In this paper, we propose a novel spatiotemporal feature descriptor based on co-occurrence matrices computed from the optical flow magnitude and orientation. Our method, called Optical Flow Co-occurrence Matrices (OFCM), extracts a robust set of measures known as Haralick features to describe the flow patterns by measuring meaningful properties such as contrast, entropy and homogeneity of co-occurrence matrices to capture local space-time characteristics of the motion through the neighboring optical flow magnitude and orientation. We evaluate the proposed method on the action recognition problem by applying a visual recognition pipeline involving bag of local spatiotemporal features and SVM classification. The experimental results, carried on three well-known datasets (KTH, UCF Sports and HMDB51), demonstrate that OFCM outperforms the results achieved by several widely employed spatiotemporal feature descriptors such as HOF, HOG3D and MBH, indicating its suitability to be used as video representation.

Land cover classification is a task that requires methods capable of learning high-level features while dealing with high volume of data. Overcoming these challenges, Convolutional Networks (ConvNets) can learn specific and adaptable features depending on the data while, at the same time, learn classifiers. In this work, we propose a novel technique to automatically perform pixel-wise land cover classification. To the best of our knowledge, there is no other work in the literature that perform pixel-wise semantic segmentation based on data-driven feature descriptors for high-resolution remote sensing images. The main idea is to exploit the power of ConvNet feature representations to learn how to semantically segment remote sensing images. First, our method learns each label in a pixel-wise manner by taking into account the spatial context of each pixel. In a predicting phase, the probability of a pixel belonging to a class is also estimated according to its spatial context and the learned patterns. We conducted a systematic evaluation of the proposed algorithm using two remote sensing datasets with very distinct properties. Our results show that the proposed algorithm provides improvements when compared to traditional and state-of-the-art methods that ranges from 5 to 15% in terms of accuracy.

The forensic detection of median filtering has recently attracted the attention of the research community, mainly because of the median filtering potential uses for tampering and concealing image tampering traces in digital images. In this paper, we propose multi-scale and multi-perturbation soluti ons that build a highly discriminative feature space, which highlights the artifacts of median filtering by means of image quality measures. The proposed methods achieve promising results when validated with a series of real-world test cases, comprising different image compression levels, resolutions, and also a cross-dataset validation protocol.

The detection of copy-move image tampering is of paramount importance nowadays, mainly due to its potential use for misleading the opinion forming process of the general public. In this paper, we go beyond traditional forgery detectors and aim at combining different properties of copy-move detection approaches by modeling the problem on a multiscale behavior knowledge space, which encodes the output combinations of different techniques as a priori probabilities considering multiple scales of the training data. Afterward, the conditional probabilities missing entries are properly estimated through generative models applied on the existing training data. Finally, we propose different techniques that exploit the multi-directionality of the data to generate the final outcome detection map in a machine learning decision-making fashion. Experimental results on complex data sets, comparing the proposed techniques with a gamut of copy-move detection approaches and other fusion methodologies in the literature, show the effectiveness of the proposed method and its suitability for real-world applications.

The performance of pattern classifiers depends on the separability of the classes in the feature space - a property related to the quality of the descriptors - and the choice of informative training samples for user labeling - a procedure that usually requires active learning. This work is devoted to improve the quality of the descriptors when samples are superpixels from remote sensing images. We introduce a new scheme for superpixel description based on Bag of visual Words, which includes information from adjacent superpixels, and validate it by using two remote sensing images and several region descriptors as baselines.

Plant phenology has been exploited as an important research venue for assessing the impact of climate changes. One common approach for monitoring vegetation relies on the use of digital cameras. The employment of imaging techniques for phenological observation allows the extraction and analysis of visual characteristics based on color and texture information with the objective of determining plant life cycle changes, such as the beginning of the leaf flushing or the senescence period. This paper presents a novel approach for detecting phenological changes by analyzing image temporal series. Our method is based on the use of visual rhythm analysis and the adoption of a dissimilarity measure to detect visual changes in the time line. Experiments were conducted on a three-year data set composed of 3,538 vegetation images and 21 samples of 6 different species of interest. Results demonstrate that the proposed change detection approach is able to effectively identify phenological events.

Increasingly, people define and express themselves in online social networks, such as Facebook and Instagram, by uploading photos showing the clothes they wear. As a result, such online social networks are becoming major sources of inspiration, with users looking for others with similar clothing style. In this paper, we propose a novel learning to rank (L2R) algorithm for finding similar apparel style given a query image. L2R algorithms use a labeled training set to generate a ranking model that can later be used to rank new query results. These training sets, however, are costly and laborious to produce, requiring human annotators to assess the relevance of candidate images in relation to a query. Active learning algorithms are able to reduce the labeling effort by selectively sampling an unlabeled set of images and choosing the subset that maximizes a learning function's effectiveness. Specifically, our proposed L2R algorithm employs an association rule active sampling algorithm to select very small but effective training sets. Further, our algorithm operates on visual (e.g., image descriptors) and textual (e.g., comments associated with the image) elements, in a way that makes it able (i) to expand the query image (for which only visual elements are available) with textual elements, and (ii) to combine multiple elements, being visual or textual, using basic economic efficiency concepts. We conducted a systematic evaluation of the proposed algorithm using every-day photos collected from Instagram, and we show that our L2R algorithm reduces by two orders of magnitude the need for labeled images, and still improves upon the state-of-the-art models by 4-8% in terms of mean average precision.

Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and the power of metaheuristic algorithms to solve combinatorial optimizations. Simulations on two public data sets have shown that the proposed framework can indeed improve the effectiveness of the OPF and considerably reduce data storage costs.

Plant phenology studies recurrent plant life cycles events and is a key component of climate change research. To increase accuracy of observations, new technologies have been applied for phenological observation, and one of the most successful are digital cameras, used as multi-channel imaging sensors to estimate color changes that are related to phenological events. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract individual plant color information and correlated with leaf phenological changes. To do so, time series associated with plant species were obtained, raising the need of using appropriate tools for mining patterns of interest. In this paper, we present a novel approach for representing phenological patterns of plant species derived from digital images. The proposed method is based on encoding time series as a visual rhythm, which is characterized by image description algorithms. A comparative analysis of different descriptors is conducted and discussed. Experimental results show that our approach presents high accuracy on identifying plant species.

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. In this context, digital cameras have been successfully used as multi-channel imaging sensors, providing measures to estimate changes on phenological events, such as leaf flushing and senescence. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. For that, we extract leaf color information and correlated with phenological changes. In this way, time series associated with plant species are obtained, raising the need of using appropriate tools for mining patterns of interest. In this paper, we present a novel approach for representing phenological patterns of plant species. The proposed method is based on encoding time series as a visual rhythm, which is characterized by color description algorithms. A comparative analysis of different descriptors is conducted and discussed. Experimental results show that our approach presents high accuracy on identifying plant species.

Many methods have been recently proposed to deal with the large amount of data provided by high-resolution remote sensing technologies. Several of these methods rely on the use of image segmentation algorithms for delineating target objects. However, a common issue in geographic object-based applications is the definition of the appropriate data representation scale, a problem that can be addressed by exploiting multiscale segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting histogram-based features from a hierarchy of regions for multiscale classification. The strategy, called H-Propagation, exploits the existing relationships among regions in a hierarchy to iteratively propagate features along multiple scales. The proposed method speeds up the feature extraction process and yields good results when compared with global low-level extraction approaches.

Hyperspectral images have been considered as one of the most important tool for remote sensed land cover analysis. Such images have information about materials on earth's surface expressed in many wavelengths that allow us to identify and classify those materials with more accuracy. In this work we used a combination of several classification methods in order to produce an accurate thematic map based on the remote sensed hyperspectral image classification. To perform the combination, three types of feature representation and two learning algorithms (Support Vector Machines (SVM) and Backpropagation Multilayer Perceptron Neural Network (MLP)) were used yielding six classification methods. Our approach proposal is based onWeighted Linear Combination (WLC), in which weights are found using Linear Programming (LP) - WLC-LP. Experiments are carried out using two well-known databases: Indian Pines, acquired by AVIRIS sensor; and Pavia University, acquired by ROSIS sensor. Results show the efficiency of our proposed approach which significantly reduces the time required to found optimal weights for the combiner compared to a previous approach based on Genetic Algorithm.

Remote phenology has motivated the development of new technologies for pattern observation. In this scenario, digital cameras have been used as data source for studies that estimate changes on phenological events. In this paper, we investigate the use of shape descriptors in the task of characterizing time series associated with phenological changes. The main objectives are: i) to determine which color channel is better for extracting shape descriptors and ii) to analyze the impact of the sunshine on the performance of shape descriptors.

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground.

Coffee crop recognition in remote sensing images is a complex task. It poses several challenges due to different spectral responses and texture patterns that can be extracted from coffee regions. This paper presents a novel framework for combining different classifiers using support vector machine technique (SVM), which try to learn with each one of classifiers previews experiences (meta-learning). We investigate the combination of seven learning methods and seven image descriptors aiming at creating low-cost classifiers for coffee crops recognition. The objective is to provide an effective mechanism for coffee crop recognition by fusion of region-based classifiers in remote sensing images. The experiments showed that the proposed framework for fusion of classifiers produces better results than the traditional majority voting fusion approach and all base classifiers tested.

In this paper we addressed the problem of dimensionality reduction in hyperspectral imagery classification by combining OPF classifier together with three recent evolutionary-based optimization algorithms: PSO, HS and GSA. We conducted experiments with two public datasets (Indian Pines and Salinas), which demonstrated that OPF combined with HS and GSA have obtained promising results, being the former the fastest approach. In regard to Indian Pines dataset, HS and GSA have achieved close classification rates, but HS has selected 46.25% less bands, which means a faster feature extraction step. For future works, we intend to provide a more detailed convergence analysis for PSO, HS and GSA, and also to introduce novel evolutionary-based band selection techniques and also to apply these methodologies for hyperspectral image classification in forest and agriculture applications.

A huge effort has been applied in image classification to create high-quality thematic maps and to establish precise inventories about land cover use. The peculiarities of remote sensing images (RSIs) combined with the traditional image classification challenges made RSI classification a hard task. Our aim is to propose a kind of boost-classifier adapted to multiscale segmentation. We use the paradigm of boosting, whose principle is to combine weak classifiers to build an efficient global one. Each weak classifier is trained for one level of the segmentation and one region descriptor. We have proposed and tested weak classifiers based on linear support vector machines (SVM) and region distances provided by descriptors. The experiments were performed on a large image of coffee plantations. We have shown in this paper that our approach based on boosting can detect the scale and set of features best suited to a particular training set. We have also shown that hierarchical multiscale analysis is able to reduce training time and to produce a stronger classifier. We compare the proposed methods with a baseline based on SVM with radial basis function kernel. The results show that the proposed methods outperform the baseline.

This work presents a new approach for automatic recognition of coffee crops in RSIs. The method applies an approach based on Genetic Programming (GP) to combine texture and spectral information encoded by image descriptors. Experiments show that the proposed method yields slightly better results than the traditional MaxVer approach.