Currently, accurate detection of natural phenomena, such as red tide, that adversely affect wildlife and human, using satellite images has been increasingly utilized. However, red tide detection on satellite images still remains a very hard task due to unpredictable nature of red tide occurrence, extreme sparsity of red tide samples, difficulties in accurate groundtruthing, etc. In this paper, we aim to tackle both the data sparsity and groundtruthing issues by primarily addressing two challenges: i) significant lack of hard examples of non-red tide that can enhance detection performance and ii) extreme data imbalance between red tide and non-red tide examples. In the proposed work, we devise a 9-layer fully convolutional network jointly optimized with two plug-in modules tailored to overcoming the two challenges: i) a hard negative example generator (HNG) to supplement the hard negative (non-red tide) examples and ii) cascaded online hard example mining (cOHEM) to ease the data imbalance. Our proposed network jointly trained with HNG and cOHEM provides state-of-the-art red tide detection accuracy on GOCI satellite images.
CNN used for Detecting Natural Phenomena in Marine Environment. Since CNN has been introduced and provided promising performance in image classification, there have been several attempts to use it in the marine environment. CNN has been effectively used for detection of coral reefs @cite_12 , classification of fish @cite_26 , detection of oil from shipwreck @cite_9 , and so on. However, applying deep neural network to detect objects-of-interest in the marine environment has been quite limited due mainly to difficulties in acquiring large amounts of annotated data unlike general object detection applications. In this paper, we devise a CNN training strategy coupled with advanced network architecture tailored to red tide detection while minimizing human labeling efforts.
@cite_9 @cite_26 @cite_12
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We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have considerably improved the performance of object recognition. However, to date there has been limited work using these deep CNNs as local feature extractors. This partly stems from CNNs having internal representations which are high dimensional, thereby making such representations difficult to model using stochastic models. To overcome this issue, we propose to reduce the dimensionality of one of the internal fully connected layers, in conjunction with layer-restricted retraining to avoid retraining the entire network. The distribution of low-dimensional features obtained from the modified layer is then modelled using a Gaussian mixture model. Comparative experiments show that considerable performance improvements can be achieved on the challenging Fish and UEC FOOD-100 datasets. Studying fish recognition has important realistic and theoretical significance to aquaculture and marine biology. Fish recognition is challenging problem because of distortion, overlap and occlusion of digital images. Previous researchers have done a lot of work on fish recognition, but the classification accuracy may be not high enough. Classification and recognition methods based on convolutional neural network (CNN) develop fast in recent years because of its higher accuracy and the support of GPU. In this paper, we design several architectures for convolutional neural network for the fish recognition. After performing a series of experiments, we can get the CNN architecture which has best performance and robustness. Coral reefs exhibit significant within-class variations, complex between-class boundaries and inconsistent image clarity. This makes coral classification a challenging task. In this paper, we report the application of generic CNN representations combined with hand-crafted features for coral reef classification to take advantage of the complementary strengths of these representation types. We extract CNN based features from patches centred at labelled pixels at multiple scales. We use texture and color based hand-crafted features extracted from the same patches to complement the CNN features. Our proposed method achieves a classification accuracy that is higher than the state-of-art methods on the MLC benchmark dataset for corals.