2 research outputs found
Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest
Detection of burn marks due to wildfires in inaccessible rain forests is
important for various disaster management and ecological studies. The
fragmented nature of arable landscapes and diverse cropping patterns often
thwart the precise mapping of burn scars. Recent advances in remote-sensing and
availability of multimodal data offer a viable solution to this mapping
problem. However, the task to segment burn marks is difficult because of its
indistinguishably with similar looking land patterns, severe fragmented nature
of burn marks and partially labelled noisy datasets. In this work we present
AmazonNET -- a convolutional based network that allows extracting of burn
patters from multimodal remote sensing images. The network consists of UNet: a
well-known encoder decoder type of architecture with skip connections commonly
used in biomedical segmentation. The proposed framework utilises stacked
RGB-NIR channels to segment burn scars from the pastures by training on a new
weakly labelled noisy dataset from Amazonia. Our model illustrates superior
performance by correctly identifying partially labelled burn scars and
rejecting incorrectly labelled samples, demonstrating our approach as one of
the first to effectively utilise deep learning based segmentation models in
multimodal burn scar identification.Comment: 5 pages, 5 figures. Accepted at IEEE International Conference on
Systems, Man and Cybernetics 2020. Earlier draft presented at Harvard CRCS AI
for Social Good Workshop 202
CognitiveCNN: Mimicking Human Cognitive Models to resolve Texture-Shape Bias
Recent works demonstrate the texture bias in Convolutional Neural Networks
(CNNs), conflicting with early works claiming that networks identify objects
using shape. It is commonly believed that the cost function forces the network
to take a greedy route to increase accuracy using texture, failing to explore
any global statistics. We propose a novel intuitive architecture, namely
CognitiveCNN, inspired from feature integration theory in psychology to utilise
human-interpretable feature like shape, texture, edges etc. to reconstruct, and
classify the image. We define two metrics, namely TIC and RIC to quantify the
importance of each stream using attention maps. We introduce a regulariser
which ensures that the contribution of each feature is same for any task, as it
is for reconstruction; and perform experiments to show the resulting boost in
accuracy and robustness besides imparting explainability. Lastly, we adapt
these ideas to conventional CNNs and propose Augmented Cognitive CNN to achieve
superior performance in object recognition.Comment: 5 Pages; LaTeX; Published at ICLR 2020 Workshop on Bridging AI and
Cognitive Scienc