Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. Unfortunately, RS images acquired by optical sensors are frequently degraded by aerosol contamination, clouds and cloud shadows, resulting in missing observations and irregular observation patterns. To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. Importantly, the developed approach remains flexible with respect to the availability of clear temporal observations. The signal derived from the ensemble of AEs is the reconstruction difference vector between input samples and their corresponding estimations, which are averaged over all cloud-/shadow-free temporal observations of a pixel location. This averaged reconstruction difference vector is the base for the representations and the subsequent classification. Experimental results show that the proposed extremely light-weight architecture indeed generates separable features for competitive performances in crop type classification, as distance metrics scores achieved with the derived representations significantly outperform those obtained with the initial data. Conventional classification models were trained and tested with representations generated from a widely used Sentinel-2 multi-spectral multi-temporal dataset, BreizhCrops. Our method achieved $77.06\%$ overall accuracy, which is $\sim 6\%$ higher than that achieved using original Sentinel-2 data within conventional classifiers and even $\sim 4\%$ better than complex deep models such as OmnisCNN. Compared to extremely complex and time-consuming models such as Transformer and long short-term memory (LSTM), only a $3\%$ reduction in overall accuracy was noted. Our method uses only $6.8k$ parameters, i.e., $\sim 400x$ fewer than OmnicsCNN and $\sim 27x$ fewer than Transformer. The results prove that our method is competitive in terms of classification performance compared with state-of-the-art methods while substantially reducing the computational load.

Remote sensing imaging datasets for classification generally present high levels of imbalance between classes of interest. This work presented a study of a set of performance evaluation metrics for an imbalance dataset. In this work, a support vector machine (SVM) was used to perform the classification of seven classes of interest in a popular dataset called Salinas-A. The performance evaluation of the classifier was performed using two types of metrics: 1) Metrics for multi-class classification, and 2) Metrics based on the binary confusion matrix. In the results, a comparison of the scores of each metric is developed, some being more optimistic than others due to the bias that they present given the imbalance. In addition, our case study helps to conclude that the Matthews correlation coefficient (MCC) presents the lowest bias in imbalanced cases and is regarded to be robust metric. These results can be extended to any imbalanced dataset taking into account the equations developed by Luque.

The increase in the increase in wildfires throughout the world is largely due to increases in temperature and even to an increase in the carelessness of the population in leaving a large amount of the garbage in forests. Using Python and Matlab programs were as working medium. We performed the preprocessing on multispectral images obtained by the Landsat 8 satellite with and without wildfires, which consists of three steps: alignment, characterization and normalization, with the intention of standardization the images. From obtaining the spectral signatures of wildfires and metallic structures, boxes and whiskers diagrams, Shannon entropy and mutual information from the images, there are similar behavior in bands 6, 7, 8, 10 and 11, with more relevant information, taking into account that each image is formed by 11 bands, and in bands 1, 2, 3, 4, 5, 8 and 9 there is less information, SVD decomposition allows to have the best k-rank approximation to the original data matrix. The purpose of this analysis is to reduce the computational complexity.

Optical remote sensing is fundamental for land monitoring, climate analysis, and agricultural applications; however, its effectiveness is often compromised by cloud contamination, which disrupts temporal continuity. Although cloud detection and masking techniques have advanced, additional gap-filling methods are needed to ensure consistent records of surface reflectance. This letter presents an enhanced version of the Harmonized and Improved Spatial and Temporal Adaptive Reflectance Fusion Model (HISTARFM) adapted for Sentinel-2 imagery. The method generates gap-filled reflectance at 10-m spatial resolution with a five-day temporal frequency, implemented on Google Earth Engine for large-scale processing. Validation across representative sites in North America, Europe, and East Asia demonstrates strong accuracy, with an average relative RMSE below 15%. The proposed approach offers a reliable framework for generating continuous, high-resolution optical datasets, thereby supporting diverse environmental, agricultural, and climate-related studies.

In recent years the floods in Mexico caused economic and human losses, therefore, it is necessary to use the possible tools that can provide help to the government to reduce damage from natural disasters. For this, we decided to develop a graphical user interface, known as GUI in Matlab for the segmentation of SAR, Multispectral and POLSAR images, with the intention of detecting flooding and vulnerable areas to flooding. The designed software compute a rivers segmentation in order to make the comparison between image with flooding and the image without flooding from the same area, and to obtain a visually result where a projection of the vulnerable areas to flooding in the original image this with help of basic segmentation algorithms such as grayscale, binarization, dilation, wavelet, normalization, filtering and edge detection.

Floodings in Mexico generated economic and human losses in recent years, so it is necessary to use all possible tools that can help the government to reduce all these disasters, especially human losses. Therefore, a Graphical User Interface (GUI) was developed in Matlab for the segmentation and classification of vegetation, water and city in multispectral images obtained from the Landsat 8 satellite with the intention of detecting floods and vulnerable zones of flooding. The interface performs a feature extraction, segmentation, classification, validation and visualization of the final results obtained through basic segmentation algorithms such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), in addition to performing the segmentation with one of the artificial intelligence methodologies most used in the state of the art: support vector machine (SVM) and the proposal of SVM with the k-nearest neighbors as an improvement to the algorithm.