This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and computational load. We also verify the benefits of fully connected conditional random fields (CRFs) as a post-processing step to improve the segmentation maps. The analysis is conducted on a set of images captured by an RGB camera aboard a UAV flying over an urban area. The dataset also contains a mask that indicates the occurrence of an endangered species called Dipteryx alata Vogel, also known as cumbaru, taken as the species to be identified. The experimental analysis shows the effectiveness of each design and reports average overall accuracy ranging from 88.9% to 96.7%, an F1-score between 87.0% and 96.1%, and IoU from 77.1% to 92.5%. We also realize that CRF consistently improves the performance, but at a high computational cost.

The Remote Sensing (RS) field continuously grapples with the challenge of transforming satellite data into actionable information. This ongoing issue results in an ever-growing accumulation of unlabeled data, complicating interpretation efforts. The situation becomes even more challenging when satellite data must be used immediately to identify the effects of a natural hazard. Self-supervised learning (SSL) offers a promising approach for learning image representations without labeled data. Once trained, an SSL model can address various tasks with significantly reduced requirements for labeled data. Despite advancements in SSL models, particularly those using contrastive learning methods like MoCo, SimCLR, and SwAV, their potential remains largely unexplored in the context of instance segmentation and semantic segmentation of satellite imagery. This study integrates SwAV within an auto-encoder framework to detect landslides using deca-metric resolution multi-spectral images from the globally-distributed large-scale landslide4sense (L4S) 2022 benchmark dataset, employing only 1% and 10% of the labeled data. Our proposed SSL auto-encoder model features two modules: SwAV, which assigns features to prototype vectors to generate encoder codes, and ResNets, serving as the decoder for the downstream task. With just 1% of labeled data, our SSL model performs comparably to ten state-of-the-art deep learning segmentation models that utilize 100% of the labeled data in a fully supervised manner.With 10% of labeled data, our SSL model outperforms all ten fully supervised counterparts trained with 100% of the labeled data.

Accurate crop type identification and crop area estimation from remote sensing data in tropical regions are still considered challenging tasks. The more favorable weather conditions, in comparison to the characteristic conditions of temperate regions, permit higher flexibility in land use, planning, and management, which implies complex crop dynamics. Moreover, the frequent cloud cover prevents the use of optical data during large periods of the year, making SAR data an attractive alternative for crop mapping in tropical regions. This paper evaluates the effectiveness of Deep Learning (DL) techniques for crop recognition from multi-date SAR images from tropical regions. Three DL strategies are investigated: autoencoders, convolutional neural networks, and fully-convolutional networks. The paper further proposes a post-classification technique to enforce prior knowledge about crop dynamics in the target area. Experiments conducted on a Sentinel-1 multitemporal sequence of a tropical region in Brazil reveal the pros and cons of the tested methods. In our experiments, the proposed crop dynamics model was able to correct up to 16.5% of classification errors and managed to improve the performance up to 3.2% and 8.7% in terms of overall accuracy and average F1-score, respectively.

The application of deep learning algorithms to Earth observation (EO) in recent years has enabled substantial progress in fields that rely on remotely sensed data. However, given the data scale in EO, creating large datasets with pixel-level annotations by experts is expensive and highly time-consuming. In this context, priors are seen as an attractive way to alleviate the burden of manual labeling when training deep learning methods for EO. For some applications, those priors are readily available. Motivated by the great success of contrastive-learning methods for self-supervised feature representation learning in many computer-vision tasks, this study proposes an online deep clustering method using crop label proportions as priors to learn a sample-level classifier based on government crop-proportion data for a whole agricultural region. We evaluate the method using two large datasets from two different agricultural regions in Brazil. Extensive experiments demonstrate that the method is robust to different data types (synthetic-aperture radar and optical images), reporting higher accuracy values considering the major crop types in the target regions. Thus, it can alleviate the burden of large-scale image annotation in EO applications.